Archives of Neurology

doi: 10.1001/archneur.64.11.1561pmid: N/A

Big Strokes in Small Persons Adams defines the clinical, imaging, and molecular pathologic findings for sickle cell–related strokes in children. He reviews the evidence for which he was a major contributor that transcranial Doppler is an effective method to show the developing features of cerebral vasculopathy requiring intervention. Through research that he and his colleagues began in 1986, they demonstrated that children with sickle cell disease who are developing high stroke risk can be detected months to years before the stroke using transcranial Doppler velocity. This major success story in neurology is graphically reviewed here. Dopamine in Drug Abuse and Addiction Volkow and colleagues review the evidence that imaging studies have provided new insights on dopamine's role in drug abuse and addiction in the human brain. These studies have shown that the reinforcing effects of drugs of abuse in humans are contingent not just on dopamine increases per se in striatum (including nucleus accumbens) but on the rate of dopamine increases: the faster the increases, the more intense the reinforcing effects. Epilepsy: Can We Count on Patient Seizure Counts? Hoppe and colleagues provide new data on the issue of patient seizure counts as a standard for individual treatment and clinical trials in epilepsy. In a comprehensive study using consecutive sampling of adult inpatients with focal epilepsies undergoing video electroencephalography monitoring, they found that patient seizure counts do not provide valid information. Editorial perspective is provided by Giridhar P. Kalamangalam, MD, DPhil; Jeremy D. Slater, MD; and James A. Ferrendelli, MD. ext-link xlink:href="ned70003"/ Defining Frontotemporal Dementia Grossman et al studied patients with frontotemporal dementia (FTD) to establish clinical, neuropsychological, and imaging features that discriminate between pathologically determined tau-positive FTD, tau-negative FTD, or frontal-variant Alzheimer disease. A discriminant function analysis grouped patients on the basis of clinical and neuropsychological features with 87.5% accuracy. Tadalafil for Erectile Dysfunction Following Spinal Cord Injury Giuliano et al studied tadalafil, a phosphodiesterase 5 (PDE5) inhibitor, to determine its efficacy and safety for use by men with erectile dysfunction (ED) secondary to traumatic spinal cord injury (SCI). The patients treated with tadalafil compared with placebo had significantly improved erectile function (Figure ), and tadalafil was well tolerated by men with ED secondary to traumatic SCI. Figure. View LargeDownload International Index of Erectile Function (IIEF) erectile function domain (EF) score after 12 weeks of treatment by erectile dysfunction (ED) severity. Patients were grouped by ED severity at baseline and then their mean IIEF-EF scores at baseline and end point were determined. P values compare the mean IIEF-EF score at end point of the tadalafil treatment group vs end point score for the placebo. Asterisk indicates statistical significance compared with placebo. Clinical Features of Pathologic Subtypes of Behavioral Variant Frontotemporal Dementia Hu and colleagues identified clinical features in behavioral variant frontotemporal dementia (bvFTD) that help to predict tau-positive pathology. They found that poor planning/judgment was associated with patients with bvFTD who had tau-positive pathology, and the constellation of impaired personal conduct and a paucity of dysexecutive symptoms identified tau-negative patients. Patterns of White Matter Atrophy in Frontotemporal Lobar Degeneration Chao et al report that patients with frontotemporal lobar degeneration who are in relatively early stages of the disease (ie, Clinical Dementia Rating scores of 1.0-1.2) have white matter atrophy that largely parallels the pattern of gray matter atrophy typically associated with these disorders. Wolff-Parkinson-White Syndrome in Patients With MELAS Sproule and colleagues investigated the frequency of Wolff-Parkinson-White (WPW) syndrome among a cohort of patients with mitochondrial encephalopathy, lactic acidosis, and strokelike episodes (MELAS) and the A3246G mutation most commonly associated with MELAS. They report that the prevalence of WPW among subjects with MELAS and the A3246G mutation appears much higher than the normal population and may become manifest earlier than neurological symptoms. Patients with MELAS should be monitored for cardiac anomalies, including cardiomyopathy and WPW. Sanfilippo Syndrome Type D Jansen et al report on the clinical and molecular data of 3 families with enzyme-based diagnoses of mucopolysaccharidosis (MPS) type IIID or Sanfilippo syndrome type D. They found that major issues in the care of patients with MPS-IIID include behavioral problems, recurrent infections, and pain from orthopedic complications. To date, all mutations in the causal gene coding for N-acetylglucosamine-6-sulfatase (type D) predict premature termination of translation, and there is no obvious genotype-phenotype correlation. Transcranial Brain Sonography in Parkinsonism Walter and colleagues used transcranial brain sonography (TCS) to exclude the diagnosis of idiopathic Parkinson disease in patients with sporadic parkinsonism. They show that distinct TCS features can exclude the diagnosis of Parkinson disease in patients with sporadic parkinsonism. Parkinsonism and Plasma Homocysteine Louis et al found that mild parkinsonian signs are associated with a higher plasma homocysteine concentration in community-dwelling patients.

Kalamangalam, Giridhar P.;Slater, Jeremy D.;Ferrendelli, James A.

doi: 10.1001/archneur.64.11.1565pmid: 17998438

The philosopher Friedrich Waismann motivated the existence of his subject as a human intellectual endeavor arising from unexpected bewilderment, akin to boarding a train, spending a few hours traveling in a single direction, and suddenly arriving at the station of original departure. “We all have our moments when something quite ordinary strikes us as queer . . . facts . . . stare at us with a puzzling expression, and we begin to wonder whether they can possibly be the things we have known all our lives. . . . ”1 In their article in this issue of the Archives, Hoppe et al2 perform such a rug-pulling maneuver from under the feet of any doctor who has ever staffed an epilepsy clinic, inquired about the patient's seizure frequency in the usual manner, made appropriate changes to prescription anticonvulsants, patted the patient on the back, arranged a review appointment, and sat back to savor a job well done. The authors' data starkly prove that, on average, patients report fewer than half of all of their seizures. Further, exhorting patients to keep an accurate tally with seizure diaries and reminders is fruitless. The implications for an individual patient are disturbing: the “seizure-free” individual may not be so; the medically controlled patient may in fact be intractable; the Engel class II surgical outcome may actually be Engel class III or worse. Equally unsettling are the authors' observations extrapolated to patient populations. For instance, how believable are comparative data from clinical drug trials that report 40% fewer seizures with drug X than with placebo? Hoppe and colleagues studied 582 seizures of partial onset in 91 patients undergoing continuous inpatient video-electroencephalographic (VEEG) monitoring. Monitoring with VEEG is the accepted objective gold standard for both seizure characterization and the syndromic classification of epilepsy.3,4 Its disadvantages are high cost and labor intensiveness. However, as a technique for the diagnosis, classification, and management of epilepsy, it is difficult to do better. Monitoring with VEEG is mandatory for the evaluation of potential surgical candidates, including those who formed the authors' study cohort. All of the patients were asked to document their seizures. Comparison was made of patient-documented seizures with those objectively found by analysis of VEEG monitoring data. The results were startling. Fifty-five percent (323 of 582) of all of the seizures went unreported. Eighty-six percent of seizures occurring out of sleep were undocumented; the corresponding figure for seizures arising in wakefulness was 32%. Seizure type significantly influenced patient reporting: simple partial seizures were reported 74% of the time; complex partial seizures, 27%. There was a tendency for seizures arising from the language-dominant hemisphere to be reported less often than seizures from the opposite side. In an ingenious design, the authors randomized their patients to either receive a daily reminder to document their seizures or not; all of the patients were told this once, at the beginning of the evaluation. The results were contrary to our expectation—there was no significant difference in seizure reporting between the 2 groups. The authors concluded the following: (1) underreporting of seizures in patients with epilepsy is significant and common, and (2) it cannot be fixed by physician encouragement to do otherwise. The message was perhaps implicit in other literature. In an outpatient setting without VEEG monitoring, a systematic approach was sufficiently revealing to Heo et al5: of their 134 subjects, only 67% were always aware of their seizures when compared with observers' accounts. In the ambulatory EEG study by Tatum et al,6 38.8% of records with seizures had at least a few seizures not signaled by button presses. Two previous VEEG monitoring studies7,8 hinted that seizures were unreported because of ictal amnesia. Errors in counting were thus not due to poor “bookkeeping”; patients were unaware of seizures even when questioned immediately after individual events. The contributions by Hoppe and colleagues are in the size of their data set (easily the largest VEEG monitoring study to date addressing this question), their clever experimental design, and their robust conclusions, intelligible to even the nonspecialist. Does all of this demolish the epileptologist's main interrogative tool, the question, “How many seizures have you had over these past few weeks?” No, we maintain. The authors' data themselves suggest how the question may be resurrected. They state that 44% of all of the seizures arose in sleep, 86% of which went unreported. We thus compute that 44% × 86% = 38% of all of the seizures were unreported nocturnal ones and 55% − 38% = 17% of all of the seizures were daytime unreported ones. The latter, and more respectable, figure probably constitutes the dropout fraction of seizures that patients can reasonably be expected to report. One resolution, therefore, is to regard the frequency of nocturnal seizures as an unknown and assess patients suspected of any nocturnal seizures via the objectivity of ambulatory EEG or VEEG monitoring. For daytime seizures, the rate of underreporting is just less than 1 in 3; acknowledge and factor this into clinical decision making, particularly in patients with complex partial seizures. Another observation by Hoppe and colleagues can be valuably turned into a “practice parameter”: their patients only activated the push-button alarm in 51 of 582 seizures (9%). If this implies that only an equivalent number of seizures had an identifiable aura, a reasonable though not infallible assumption,9 then most seizures (approximately 90%) were not prefaced by auras. Recognizing that patient memory of a seizure is usually that of the aura, we suggest that if patients do not report preictal auras, their physicians should not believe their reported seizure frequency. Conversely, do patients with auras report their seizures more consistently? This is a question not settled in the work by Hoppe and colleagues but clearly worthy of further study. In summary, Hoppe and colleagues convey important lessons regarding the objectivity of seizure reporting by patients. The implications of this valuable work for large-scale clinical trials, population-based questionnaires, and other epidemiologic studies are immediate. For the individual practitioner, the main lesson is acknowledging the pitfalls of routine clinical questioning of patients with seizures, especially those with complex partial seizures. However, the lack of accuracy of patient self-reporting can be ameliorated, we observe from the authors' own data, by disambiguating nocturnal and daytime seizures on the one hand and seizures with and without aura on the other. Finally, what about patients with generalized epilepsies, patients unable to self-report seizures for other reasons, children, etc? Finding out will be interesting and important. On a different point, we can only advocate the wider use of VEEG monitoring in the clinical practice of treating epilepsy. An alternative to VEEG monitoring is the development of better ambulatory technology; current methods of prolonged outpatient multichannel EEG recording are difficult to perform and to interpret for a host of reasons. We suggest that future developments in this area be directed to robustly answering (whether through EEG, electromyography, or autonomic function monitoring) just the single question, how many seizures? Back to top Article Information Correspondence: Dr Ferrendelli, Department of Neurology, University of Texas Health Science Center, 7.102 MSB, 6431 Fannin St, Houston, TX 77030 ([email protected]). Author Contributions:Study concept and design: Kalamangalam, Slater, and Ferrendelli. Analysis and interpretation of data: Kalamangalam, Slater, and Ferrendelli. Drafting of the manuscript: Kalamangalam. Critical revision of the manuscript for important intellectual content: Kalamangalam, Slater, and Ferrendelli. Administrative, technical, and material support: Kalamangalam. Study supervision: Kalamangalam, Slater, and Ferrendelli. Financial Disclosure: None reported. References 1. Waismann F How I see philosophy. In: Titus HH, Hepp MH, eds. The Range of Philosophy.2nd ed. New York, NY: Van Nostrand Reinhold Co; 1970Google Scholar 2. Hoppe CPoepel AElger CE Epilepsy: accuracy of patient seizure counts. Arch Neurol 2007;64 (11) 1595- 1599Google ScholarCrossref 3. Thompson JLEbersole JS Long-term inpatient audiovisual scalp EEG monitoring. J Clin Neurophysiol 1999;16 (2) 91- 99PubMedGoogle ScholarCrossref 4. Nordli DR Usefulness of video-EEG monitoring. Epilepsia 2006;47(suppl 1)26- 30PubMedGoogle ScholarCrossref 5. Heo KHan S-DLim SRKim MALee BI Patient awareness of complex partial seizures. Epilepsia 2006;47 (11) 1931- 1935PubMedGoogle ScholarCrossref 6. Tatum WOWinters LGieron M et al. Outpatient seizure identification: results of 502 patients using computer-assisted ambulatory EEG. J Clin Neurophysiol 2001;18 (1) 14- 19PubMedGoogle ScholarCrossref 7. Blum DEEskola JBortz JJFisher RS Patient awareness of seizures. Neurology 1996;47 (1) 260- 264PubMedGoogle ScholarCrossref 8. Kerling FMueller SPauli EStefan H When do patients forget their seizures? an electroclinical study. Epilepsy Behav 2006;9 (2) 281- 285PubMedGoogle ScholarCrossref 9. Block AFisher RS Can patients perform volitional motor acts at the start of a seizure? J Clin Neurophysiol 1999;16 (2) 141- 145PubMedGoogle ScholarCrossref

Adams, Robert J.

doi: 10.1001/archneur.64.11.1567pmid: 17998439

Abstract Sickle cell disease (SCD) is understood on a genetic and a molecular level better than most diseases. Young children with SCD are at a very high risk of stroke. The molecular pathologic abnormalities of SCD lead to microvascular occlusion and intravascular hemolytic anemia. Microvascular occlusion is related to painful episodes and probably causes microcirculatory problems in the brain. The most commonly recognized stroke syndrome in children with SCD is large-artery infarction. These “big strokes” are the result of a vascular process involving the large arteries of the circle of Willis leading to territorial infarctions from perfusion failure or possibly artery-to-artery embolism. We can detect children who are developing cerebral vasculopathy using transcranial Doppler ultrasonography (TCD) and can provide effective intervention. Transcranial Doppler ultrasonography measures blood flow velocity in the large arteries of the circle of Willis. Velocity is generally increased by the severe anemia in these patients, and it becomes elevated in a focal manner when stenosis reduces the arterial diameter. Children with SCD who are developing high stroke risk can be detected months to years before the stroke using TCD. Healthy adults have a middle cerebral artery velocity of approximately 60 cm/s, whereas children without anemia have velocities of approximately 90 cm/s. In SCD, the mean is approximately 130 cm/s. Two independent studies have demonstrated that the risk of stroke in children with SCD increases with TCD velocity. The Stroke Prevention Trial in Sickle Cell Anemia (STOP) (1995-2000) was halted prematurely when it became evident that regular blood transfusions produced a marked (90%) reduction in first stroke. Children were selected for STOP if they had 2 TCD studies with velocities of 200 cm/s or greater. Children not undergoing transfusion had a stroke risk of 10% per year, which was reduced to less than 1% per year by regular blood transfusions. Stroke risk in all children with SCD is approximately 0.5% to 1.0% per year. On the basis of STOP, if the patient meets the high-risk TCD criteria, regular blood transfusions are recommended. A second study was performed (2000-2005) to attempt withdrawal of transfusion in selected children in a randomized controlled study. Children with initially abnormal TCD velocities (≥200 cm/s) treated with regular blood transfusion for 30 months or more, which resulted in reduction of the TCD to less than 170 cm/s, were eligible for randomization into STOP II. Half continued transfusion and half had cessation of transfusion. This trial was halted early for safety reasons. There was an unacceptably high rate of TCD reversion back to high risk (≥200 cm/s), as well as 2 strokes in children who discontinued transfusion. There are no evidence-based guidelines for the discontinuation of transfusion in children once they have been identified as having high risk based on TCD. The current situation is undesirable because of the long-term effects of transfusion, including iron overload. Iron overload has recently become easier to manage with the introduction of an oral iron chelator. The inflammatory environment known to exist in SCD and the known effect of plasma free hemoglobin, released by hemolysis, of reducing available nitric oxide may contribute to the development of cerebrovascular disease. Further research may lead to more targeted therapies. We can reduce many of the big strokes that occur in these small persons by aggressively screening patients at a young age (and periodically throughout the childhood risk period) and interrupting the process with regular blood transfusions. In 1910, American physician James Herrick, MD, published the first case in the Western medical literature on sickle cell disease (SCD) in a Grenadian dental student living in Chicago, Illinois. A blood smear from this student showed “peculiar elongated cells.”1 Pauling et al drew attention to SCD as a “molecular disease.”2 Much about SCD is understood. There are approximately 80 000 patients with the most severe form of the disease in the United States, and more than 15 000 articles have been cited in PubMed on the topic since 1949. Well before the major biological features of this genetic disorder were elucidated, stroke had been noted as an associated complication of SCD.3 Early brain pathologic studies described many abnormalities of the brain and its vasculature, and in a few cases, large cerebral infarctions were reported.4 Why would a blood disorder, characterized by severe hemolytic anemia and known to engender pathophysiologic features on a microscopic scale,5 lead to stroke at all, much less large brain infarctions? Most of the neurologic reviews published before 1976 emphasized the presumed basis for circulatory problems, including stroke arising from occlusion of small vessels by the sickled erythrocytes that give the disease its name.6 These were known to be formed, at first reversibly, then finally into a state of permanent distortion, when cells containing sickle hemoglobin and insufficient other hemoglobins (such as fetal hemoglobin, which retards sickling) become deoxygenated.7 The cellular distortion is caused by sickle cell hemoglobin forming intracellular polymers. Although sickle cell hemoglobin carries oxygen in a similar manner as hemoglobin A in solution (although the oxygen dissociation curve is shifted to the right compared with normal), its presence in cells in the deoxygenated state leads to ischemia by reducing the ability of the erythrocyte to traverse the microcirculation. These cells are also subject to premature destruction, which leads to severe anemia and also releases toxic elements (eg, plasma free hemoglobin) into the circulation. The fundamental paradigm for the most common clinical feature, the painful “crisis,” is now understood as a sequence of events that begins with attachment of large immature red blood cells (RBCs) (reticulocytes) to the postcapillary venule and propagation backward until there is delayed transit time of RBCs, causing further deoxygenation and sickling. These events take place in vessels the size of RBCs that measure 5 to 10 μm.5 Recent data have also implicated RBC-leukocyte and leukocyte-endothelial interactions, which may precede the RBC-endothelial attachment and do not take place in mice deficient in E selectin and P selectin.8 Although the microcirculatory events have been studied ex vivo, as yet there is no comparable large-vessel animal model that would allow a better understanding of the events leading to occlusion of arteries such as the middle cerebral or internal carotid. How the genetic disorder of sickle cell anemia creates large arterial vasculopathy and leads to stroke remains somewhat of a mystery, but the schema proposed by Platt9 is a good approximation of what is believed to happen in large brain arteries before stroke (Figure 1). The publication in 1972 by Stockman et al10 of cerebral angiography in 7 patients with SCD and neurologic complications drew the first real attention to the fact that large intracerebral arteries were sometimes involved in a stenotic and obliterative process10 preferentially located just beyond the origin of the ophthalmic artery and involving part or all of the anterior circle of Willis. The example of advanced cerebrovascular disease shown in Figure 2 is based on studies performed on an 8-year-old boy who had a stroke and was studied using angiography and transcranial Doppler ultrasonography (TCD) early in our project at the Medical College of Georgia. It shows several important and typical features. On the side with stroke is occlusion of the internal carotid artery just distal to the origin of the ophthalmic artery. The TCD shows a very low and blunted waveform. On the opposite side, as yet without stroke but at risk, is severe stenosis that, while still allowing flow, has the characteristic high-velocity TCD “signature” that has become the basis for presymptomatic screening and institution of preventive therapy before brain infarction. In this case, the velocity is approximately 230 cm/s, well above what would later become the threshold for prophylactic treatment of 200 cm/s. After the study by Stockman et al,10 there followed further confirmation that large-artery disease of the internal carotid and middle cerebral arteries is typically found in many but not all children with brain infarction and in approximately half of those with intracranial hemorrhage.11 Angiography in the other cases of hemorrhage showed only diffusely dilated arteries or aneurysms that caused subarachnoid hemorrhage. We began working on this problem in 1985 at the Medical College of Georgia when a young girl with SCD had a massive infarction after internal carotid artery occlusion. Russell et al12 had just published an article with extensive angiographic documentation of what Stockman et al and others had reported and also suggested that regular transfusion might, if not reverse the process, at least prevent arterial worsening. Their study, while crucial, was not a clinical trial, and in fact no trial for secondary stroke prevention in SCD has yet been published. However, regular blood transfusions soon became an accepted therapy to prevent recurrent stroke in SCD based on comparison with historically high recurrence rates in children with SCD. In 1982, Aaslid et al13 published an article on TCD for the detection of subarachnoid hemorrhage–related vasospasm. In 1985, the Medical College of Georgia SCD Cohort Study began using TCD in patients along with magnetic resonance imaging (MRI). Although both of the new techniques have much to offer in the study of stroke and patients at risk for stroke, it was to be TCD that proved more useful for primary stroke prevention in these patients, largely because it was so much easier to use. In children with homozygous SCD, a yearly first stroke risk of approximately 0.5% had been established by the Cooperative Study of Sickle Cell Disease based on a large population study14 that predated TCD and MRI use (Table 1). Although some risk factors for stroke were identified (slightly lower total hemoglobin level, transient ischemic attack, and acute chest syndrome), a predictive model sufficient to plan a clinical prevention trial was not available. This stroke rate, which is very high for children, was still too low to execute a practical randomized controlled trial given the limited number of patients available. Even assuming that transfusion was completely effective in preventing the first stroke, without a way to select the children at greatest risk, one would have to transfuse approximately 200 children each year to prevent 1 stroke. Primary stroke prevention would depend on finding an acceptable way to identify individuals at highest risk to make the number needed to treat more attractive and manageable. It seemed reasonable to focus on the large intracranial arteries rather than the brain itself for 2 reasons. First, selecting patients on the basis of infarction that had already taken place meant that we would be “following” the process of brain injury rather than preventing it firsthand. Second, the collective angiographic information, derived from children who had already had a stroke, had shown that major arteries opposite the hemispheres that had evident brain infarction often showed early or sometimes extensive arterial narrowing that had not yet become symptomatic. This suggested that the arterial process leading to large strokes might develop at a slow enough rate to create a “window of intervention” before the brain is affected in children developing vasculopathy and on this basis the high-risk state for stroke. Using angiography to screen large numbers of children who were asymptomatic did not seem practical. El Gammal et al22 and Pavlakis et al23 at Columbia began using MRI, but MR angiography (MRA) was not yet available. The TCD seemed to be a possible method, but it was not clear whether it would work in this application. Use of Doppler ultrasonography had become an important way to identify extracranial carotid stenosis based on a derivation of the Bernoulli principle, which is that in the area where the vessel is narrowed, the velocity of blood flowing in that vessel is increased. However, cervical Doppler ultrasonography would at best reflect flow changes secondary to intracranial stenosis because the internal carotid artery in SCD is rarely affected by vasculopathy; TCD seemed a more direct diagnostic approach. The TCD was also attractive because it was harmless, painless, and generally well tolerated, even by children, and did not require sedation. It was also portable and low cost compared with other methods. However, in SCD there were specific challenges in using TCD. Would these young children with so many other medical issues tolerate the examination? What velocity standards should be used? It was known that cerebral blood flow and flow velocity as estimated by TCD are increased in the anemic condition owing to the lowered oxygen-carrying capacity of the blood. It was soon evident from work by Brass et al24 at Columbia and Adams et al25,26 that the evolving velocity standards of TCD for stenosis in adults would not apply in SCD owing to the impact of young age and anemia. Would there be sufficient separation between children with elevated TCD velocities due to anemia with no vessel disease and average risk and those with anemia who were developing stenosis and increased risk? To determine whether TCD would predict stroke, a series of studies were performed between 1985 and 1992. The first study was to establish the expected middle cerebral artery velocity in children of the target age, with25 and without26 SCD and without stroke. Transcranial Doppler ultrasonography was also performed on children with stroke at the time of cerebral angiography, providing an opportunity to correlate velocity to stenosis visualized using that method.15 In parallel, a large cohort of children with SCD but no stroke history and not undergoing regular transfusion were recruited, studied using TCD, and followed up prospectively for stroke outcome.16,27 The portability and ease of use of TCD was instrumental because many of these TCD studies were performed in outreach clinics across Georgia during regular clinic visits. This feature of TCD greatly enhanced enrollment and follow-up. The early use of TCD in adults had shown that the normal velocity (time-averaged mean of maximum blood flow as opposed to systolic or diastolic) was approximately 60 cm/s (Table 2). It was determined that the expected middle cerebral artery velocity in healthy children was approximately 90 cm/s, and in children with SCD without overt stroke it was approximately 130 cm/s, elevated on the basis of young age and severe anemia. When the angiogram showed severe stenosis, the velocity was always greater than 190 cm/s and often much higher except in cases of severe stenosis, in which very low velocities were recorded (<70 cm/s).15 These data provided key cross-sectional information, but what was needed to make primary stroke prevention possible was to demonstrate that TCD predicted risk of future stroke. That elevated TCD velocity was associated with a high risk of future stroke was demonstrated in the Medical College of Georgia SCD Cohort study. The long-term outcome of 315 children aged 3 to 18 years at the time of first screening and free of stroke when enrolled was evident from the stroke-free survival curves. Arbitrary velocity cutoff points (there are no evident “inflection points” in the risk relationship, but cutoff points were needed to define risk strata) were used to stratify risk: less than 170 cm/s represented “normal” or average risk, 170 to 199 cm/s was called “conditional” and was associated with moderate risk, and 200 cm/s or greater was called “abnormal” or high risk. A risk of stroke during the next 36 months of 13% per year was observed.16,27 Although the time from abnormal TCD velocity to stroke was variable, children with the highest velocity seemed to have more proximate risk (5 children with TCD velocity >240 cm/s had a stroke within 9 months of TCD). Armed with this information, a proposal was made to the National Heart, Lung, and Blood Institute to fund the first randomized controlled trial of stroke prevention for any indication in SCD and the first primary stroke prevention trial in children with any disease. This study, STOP, was conducted between 1995 and 2000 at 14 sites in the United States and Canada.28 A unique feature was the selection of participants based on TCD. Almost 2000 children aged 2 to 16 years with no history of stroke were screened using TCD, and those with 2 TCDs showing a middle cerebral artery or internal carotid artery velocity of 200 cm/s or higher were approached for randomization. Rigorous operator training and standardization of TCD protocol and equipment were used to ensure uniform testing. Screening for high-risk cases in STOP showed the same prevalence of TCD findings at all 14 sites (and was similar to that found in the Medical College of Georgia cohort): approximately 10% had 1 TCD, and 85% of those with 1 abnormal TCD were confirmed to have abnormal results on a second study. The final high-risk rate was approximately 9%; approximately 15% fell into a “conditional” range of 170 to 199 cm/s, and 70% had velocities less than 170 cm/s, falling into a lower-risk group. The others had inadequate studies that had to be repeated. The TCDs were recorded as digital files, which facilitated file transfer between the research sites and the data and reading centers, study masking, and blinded reading. Neither MRI nor MRA was performed until after randomization, and imaging for “silent stroke” was evaluated as a secondary end point.28,29 The trial was halted 16 months early when 11 of the 67 children randomized to receive standard care (episodic transfusions only depending on symptoms such as pain) had a stroke compared with a single child undergoing long-term transfusion.21 The observed stroke rate without transfusion was 10% per year across 2 years, confirming the reliability and validity of TCD in a multicenter application and the dramatic (>90%) reduction in stroke with regular transfusion (Figure 3). One surprising finding was that MRAs of cerebral vessels in children with velocities greater than 200 cm/s but less than 250 cm/s did not usually show severe stenosis,30 suggesting that TCD indicates risk at an earlier (and probably more reversible) stage than MRA. The STOP results were announced in 1997. Despite the clear results and recommendations based on STOP from the National Heart, Lung, and Blood Institute31 and the American Stroke Association (also endorsed by the American Academy of Neurology)20 for widespread screening and prophylactic transfusion in high-risk cases, the long-term problems with transfusion, and the fact that a stopping point for transfusion was not clear, some physicians remained hesitant to adopt this strategy. In STOP, many patients undergoing transfusion saw their TCD velocities revert to apparent low risk (<170 cm/s; approximately 53%) or intermediate risk (170-199 cm/s; approximately 17%), especially if the velocity at treatment initiation was in the low abnormal range (200-230 cm/s) and the MRA findings were relatively normal (see Figure 4 and Figure 5 for an example of change with regular transfusion). Approximately 30% of children who were compliant with regular transfusion still had abnormal TCD velocities even after years of transfusion. A second study was then planned to determine whether continued treatment was still needed in the subset that normalized with prolonged transfusion. The STOP II32 was designed to determine whether transfusion could be safely withdrawn after a defined treatment period with acceptable stroke risk and rates of return to transfusion. This trial was planned to enroll 100 children, all with originally high-risk TCD velocities that reverted to less than 170 cm/s after at least 30 months of transfusion and who had an MRA that did not show moderate or severe stenosis or occlusion. The design called for half the children to be randomized to continued transfusion and half to have transfusion stopped. All the participants received close follow-up and TCD at least every 12 weeks. This trial was also stopped after 74 patients were enrolled when it was clear that stopping transfusion was associated with rapid reversion of TCD velocities to high-risk levels only in children who stopped transfusion (Figure 6). Two strokes occurred in children who had reversion to abnormal TCD velocities and before the reinstitution of transfusion.32 Even in this low-risk subset (those with severe stenosis on MRA and whose TCD velocity did not normalize after ≥30 months of transfusion were not included), by the end of 1 year, more than half of those who discontinued transfusion had restarted it. Although the long-term problems of transfusion, especially iron overload, can be predicted and managed, new approaches that limit the long-term use of this powerful but intensive therapy are needed. Although there are theories (see Figure 1 proposed by Platt9) as to how circle of Willis vasculopathy develops, there are no animal models. Much attention has been given to the abnormal tenacity with which RBCs (especially immature cells that contain sickle hemoglobin) adhere to the endothelium in cultured cell preparations as a cause of vascular occlusion generally in SCD. White blood cells and platelets are probably involved to some extent as well. How RBC adherence might initiate or promote vascular injury and eventual stenosis of large arteries, such as those of the circle of Willis, requires further study. A connection between SCD and nitric oxide has become a subject of increasing interest in the past several years.33 It has long been known that plasma free hemoglobin inactivates nitric oxide. The hemolytic anemia of SCD releases significant amounts of free hemoglobin that comes into contact with the endothelial surface. This is believed to cause abnormalities in vascular function, which could include impairment of vasodilation, one of the functions that nitric oxide plays in systemic circulation. Although it is relatively easy to see how consumption of nitric oxide and a secondary feature of hemolysis, an increase in arginase activity in the serum, which further reduces the available nitric oxide by depleting the substrain, lead to pulmonary hypertension by reduction in vasodilation, it is less easy to see how aberrations in nitric oxide might lead to cerebral vasculopathy. Nitric oxide has other functions, such as reduction in inflammatory mediators, and this may contribute to cerebral vasculopathy. An animal model that mimics the development of abnormally high cerebral blood flow rates and the development of large-vessel vasculopathy in the circle of Willis is a critical need to understand how nitric oxide and other metabolic factors contribute to stroke risk in SCD. Finally, there is an encouraging indication that the approach proved in STOP may be working to reduce stroke. Fullerton et al34 evaluated administrative data in California comparing the rates of hospital admission for first stroke in children with SCD between the early 1990s (before STOP) and from 1998 to 2000 (after STOP) and found a sharp reduction in first stroke admissions, whereas overall SCD and SCD pain admissions did not decline. Such data do not establish a long-term trend or prove that the change is due to use of the STOP primary prevention strategy, but they are encouraging. While we try to learn more about why these large strokes afflict these small patients, we do have a way to make a positive impact now. Early and repeated screening should result in reduction in the prevalence of severe arterial disease and stroke in SCD, albeit at the price of extensive use of transfusion35 until other therapies are developed. Considering the reports from the 2 STOP trials36 still to be published and 2 currently ongoing clinical trials in children with SCD, one testing other approaches to screening (Silent Cerebral Infarct Multi-Center Clinical Trial)37 and the other testing hydroxyurea compared with transfusion for secondary stroke prevention (Stroke With Transfusions Changing to Hydroxyurea trial),38 we can look forward to extensive data from 4 randomized controlled trials aimed at stroke prevention in children with SCD. Back to top Article Information Correspondence: Robert J. Adams, MS, MD, South Carolina Center of Economic Excellence, Medical University of South Carolina Stroke Center, 96 Jonathan Lucas St, Charleston, SC 29425 ([email protected]). Accepted for Publication: January 18, 2007. Financial Disclosure: Dr Adams has given lectures sponsored by Boehringer Ingelheim, Bristol-Myers Squibb, Sanofi-Aventis, and Novartis; has served as a consultant for Boehringer Ingelheim, Bristol-Myers Squibb, Sanofi-Aventis, and Merck Pharmaceuticals; has served on advisory boards for Boehringer Ingelheim, Bristol-Myers Squibb, and Sanofi-Aventis; has received small, unrestricted educational grants or support for training courses from Boehringer Ingelheim, Bristol-Myers Squibb, and Nicolet; and receives honoraria from Novartis for speaking about SCD prevention and the management of iron overload. Funding/Support: This study was funded by the National Institutes of Health. Additional Contributions: I thank the study participants from the Medical College of Georgia SCD Cohort Study, STOP, and STOP II and the many investigators and staff for their work on these studies; Thomas Robert Swift, MD, for his enduring encouragement throughout my career, and several colleagues, including Virgil McKie, MD, and Kathy McKie, MD, the very dedicated pediatricians who early on invested their trust and their patients' well-being in this research, thereby making it possible; Fenwick Nichols, MD, for his expert assistance with TCD and his knowledge of the field; Don Brambilla, PhD, for serving as the statistical expert and co-investigator, and Dianne Gallagher, MS, Suzanne Granger, MS, and many others on the staff at New England Research Institutes; Robert Zimmerman, MD, for leading the imaging review core; Steve Roach, MD, for leading the event adjudication core; the many dedicated physicians, research nurses, and coordinators for carrying out the day-to-day work; my staff (Betsy Carl Rhode, Nadine Odo, and Judi Schweitzer, to name only a few) for their long service and assistance; the research support staff at the Medical College of Georgia; Anne Jones, RN, for volunteering for more than 15 years to advance this work; Mike Jensen of the Medical College of Georgia, for preparing the figures for this article; Judy Luden, for performing and reading more than 10 000 TCDs to help reduce childhood stroke; Ann Sapp and Judi Schweitzer, for helping with the manuscript; my wife, Gaye Adams, MD, for her faithful support; my son Chris, for volunteering as a TCD training subject many times in the early days; and my family for their encouragement. We remember Charles Pegelow, MD, David Ode, MD, and Katie Allen, RN, who contributed to this work; the several patients who died during STOP and STOP II; and Larry Brass, MD, who worked in this field early in his career and who died in 2006. References 1. Herrick JB Peculiar elongated and sickle-shaped red corpuscles in a case of severe anemia. Arch Intern Med 1910;6517- 521Google ScholarCrossref 2. Pauling LItano HASinger SJWells IC Sickle cell anemia: a molecular disease. Science 1949;110 (2865) 543- 548PubMedGoogle ScholarCrossref 3. Syndenstricker VPMulherin WAHouseal RW Sickle cell anemia. AJDC 1923;26132- 154Google Scholar 4. Bridgers WA Cerebral vascular disease accompanying sickle cell anemia. Am J Pathol 1939;15353- 362Google Scholar 5. Steinberg MHBrugnara C Pathophysiological-based approaches to treatment of sickle cell disease. Annu Rev Med 2003;5489- 112PubMedGoogle ScholarCrossref 6. Baird RLWeiss DLFerguson ADFrench JHScott RB Studies in sickle cell anemia: clinical-pathological aspects of neurological manifestations. Pediatrics 1964;3492- 100PubMedGoogle Scholar 7. Bunn HF Pathogenesis and treatment of sickle cell disease. N Engl J Med 1997;337 (11) 762- 769PubMedGoogle ScholarCrossref 8. Turhan AWeiss LAMohandas NColler BSFrenette PS Primary role for adherent leukocytes in sickle cell vascular occlusion: a new paradigm. Proc Natl Acad Sci U S A 2002;99 (5) 3047- 3051PubMedGoogle ScholarCrossref 9. Platt OS Preventing stroke in sickle cell anemia. N Engl J Med 2005;353 (26) 2743- 2745PubMedGoogle ScholarCrossref 10. Stockman JANigro MAMishkin MMOski FA Occlusion of large cerebral vessels in sickle-cell anemia. N Engl J Med 1972;287 (17) 846- 849PubMedGoogle ScholarCrossref 11. Adams RJNichols FT Sickle cell anemia, sickle cell trait and thalassemia. In: Vinken PJ, Bruyn GW, Klawans HL, eds. Vascular Diseases: Part III. Amsterdam, the Netherlands: Elsevier Science Publications; 1989:503-515. Handbook of Clinical Neurologyvol 11Google Scholar 12. Russell MOGoldberg HIHodson A et al. Effect of transfusion therapy on arteriographic abnormalities and on recurrence of stroke in sickle cell disease. Blood 1984;63 (1) 162- 169PubMedGoogle Scholar 13. Aaslid RMarkwalder TMNornes H Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries. J Neurosurg 1982;57 (6) 769- 774PubMedGoogle ScholarCrossref 14. Ohene-Frempong KWeiner SJSleeper LA et al. Cerebrovascular accidents in sickle cell disease: rates and risk factors. Blood 1998;91 (1) 288- 294PubMedGoogle Scholar 15. Adams RJNichols FTFigueroa RMcKie VCLott T Transcranial Doppler correlation with cerebral angiography in sickle cell disease. Stroke 1992;23 (8) 1073- 1077PubMedGoogle ScholarCrossref 16. Adams RJMcKie VCCarl EM et al. Long-term stroke risk in children with sickle cell disease screened with transcranial Doppler. Ann Neurol 1997;42 (5) 699- 704PubMedGoogle ScholarCrossref 17. Broderick JTalbot GTPrenger ELeach ABrott T Stroke in children within a major metropolitan area: the surprising importance of intracerebral hemorrhage. J Child Neurol 1993;8 (3) 250- 255PubMedGoogle ScholarCrossref 18. Miller STMacklin EAPegelow CH et al. Cooperative Study of Sickle Cell Disease, Silent infarction as a risk factor for overt stroke in children with sickle cell anemia: a report from the Cooperative Study of Sickle Cell Disease. J Pediatr 2001;139 (3) 385- 390PubMedGoogle ScholarCrossref 19. Pegelow CHAdams RJMcKie VC et al. Risk of recurrent stroke in patients with sickle cell disease treated with erythrocyte transfusions. J Pediatr 1995;126 (6) 896- 899PubMedGoogle ScholarCrossref 20. Goldstein LBAdams RJBecker K et al. Primary prevention of ischemic stroke: a statement for healthcare professionals from the Stroke Council of the American Heart Association. Stroke 2001;32 (1) 280- 299PubMedGoogle ScholarCrossref 21. Adams RJMcKie VCHsu L et al. Prevention of a first stroke by transfusions in children with sickle cell anemia and abnormal results on transcranial Doppler ultrasonography. N Engl J Med 1998;339 (1) 5- 11PubMedGoogle ScholarCrossref 22. el Gammal TAdams RJNichols FT et al. MR and CT investigation of cerebrovascular disease in sickle cell patients. AJNR Am J Neuroradiol 1986;7 (6) 1043- 1049PubMedGoogle Scholar 23. Pavlakis SGBello JProhovnik I et al. Brain infarction in sickle cell anemia: magnetic resonance imaging correlates. Ann Neurol 1988;23 (2) 125- 130PubMedGoogle ScholarCrossref 24. Brass LMPavlakis SGDeVivo D et al. Transcranial Doppler measurements of the middle cerebral artery: effect of hematocrit. Stroke 1988;19 (12) 1466- 1469PubMedGoogle ScholarCrossref 25. Adams RJNichols FTMcKie VC et al. Transcranial Doppler: influence of hematocrit in children with sickle cell anemia without stroke. J Cardiovasc Ultrasonogr 1989;8 (2) 97- 101Google Scholar 26. Adams RJNichols FTStephens S et al. Transcranial Doppler: the influence of age and hematocrit in normal children. J Cardiovasc Ultrasonogr 1988;7 (3) 201- 205Google Scholar 27. Adams RMcKie VCNichols FT et al. The use of transcranial ultrasonography to predict stroke in sickle-cell disease. N Engl J Med 1992;326 (9) 605- 610PubMedGoogle ScholarCrossref 28. Adams RJMcKie VCBrambilla DJ et al. Stroke prevention trial in sickle cell anemia. Control Clin Trials 1998;19 (1) 110- 129PubMedGoogle ScholarCrossref 29. Zimmerman RA MRI/MRA evaluation of sickle cell disease of the brain. Pediatr Radiol 2005;35 (3) 249- 257PubMedGoogle ScholarCrossref 30. Abboud MRCure JGranger S et al. Magnetic resonance angiography in children with sickle cell disease and abnormal transcranial Doppler ultrasonography findings enrolled in the STOP study. Blood 2004;103 (7) 2822- 2826PubMedGoogle ScholarCrossref 31. National Heart, Lung, and Blood Institute Clinical alert: periodic transfusions lower stroke risk in children with sickle cell anemia. http://www.nlm.nih.gov/databases/alerts/sickle97.html. Accessed August 29, 2007 32. Adams RJBrambilla DOptimizing Primary Stroke Prevention in Sickle Cell Anemia (STOP 2) Trial Investigators, Discontinuing prophylactic transfusions to prevent stroke in sickle cell disease. N Engl J Med 2005;353 (26) 2769- 2778PubMedGoogle ScholarCrossref 33. Rother RPBell LHillmen PGladwin MT The clinical sequelae of intravascular hemolysis and extracellular plasma hemoglobin: a novel mechanism of human disease. JAMA 2005;293 (13) 1653- 1662PubMedGoogle ScholarCrossref 34. Fullerton HJJohnston SCZhao SAdams RJ Declining rates in Californian children with sickle cell disease. Blood 2004;104 (2) 336- 339PubMedGoogle ScholarCrossref 35. Adams RJPavlakis SRoach ES Sickle cell disease and stroke: primary prevention and transcranial Doppler. Ann Neurol 2003;54 (5) 559- 563PubMedGoogle ScholarCrossref 36. Lee MTPiomelli SGranger S et al. Stroke Prevention Trial in Sickle Cell Anemia (STOP): extended follow-up and final results. Blood 2006;108 (3) 847- 852PubMedGoogle ScholarCrossref 37. ClinicalTrials.gov Web site Silent Cerebral Infarct Multi-Center Clinical Trial. http://www.clinicaltrials.gov/ct/show/NCT00072761. Accessed August 29, 2007 38. ClinicalTrials.gov Web site Stroke With Transfusions Changing to Hydroxyurea (SWiTCH). http://www.clinicaltrials.gov/ct/show/NCT00122980. Accessed August 29, 2007

Volkow, Nora D.;Fowler, Joanna S.;Wang, Gene-Jack;Swanson, James M.;Telang, Frank

doi: 10.1001/archneur.64.11.1575pmid: 17998440

Abstract Imaging studies have provided new insights on the role of dopamine (DA) in drug abuse and addiction in the human brain. These studies have shown that the reinforcing effects of drugs of abuse in human beings are contingent not just on DA increases per se in the striatum (including the nucleus accumbens) but on the rate of DA increases. The faster the increases, the more intense the reinforcing effects. They have also shown that elevated levels of DA in the dorsal striatum are involved in the motivation to procure the drug when the addicted subject is exposed to stimuli associated with the drug (conditioned stimuli). In contrast, long-term drug use seems to be associated with decreased DA function, as evidenced by reductions in D2 DA receptors and DA release in the striatum in addicted subjects. Moreover, the reductions in D2 DA receptors in the striatum are associated with reduced activity of the orbitofrontal cortex (region involved with salience attribution and motivation and with compulsive behaviors) and of the cingulate gyrus (region involved with inhibitory control and impulsivity), which implicates deregulation of frontal regions by DA in the loss of control and compulsive drug intake that characterizes addiction. Because DA cells fire in response to salient stimuli and facilitate conditioned learning, their activation by drugs will be experienced as highly salient, driving the motivation to take the drug and further strengthening conditioned learning and producing automatic behaviors (compulsions and habits). Dopamine (DA) is the neurotransmitter that has been classically associated with the reinforcing effects of drugs of abuse and may have a key role in triggering the neurobiological changes associated with addiction. This notion reflects the fact that all of the drugs of abuse increase the extracellular concentration of DA in the nucleus accumbens. Increases in DA levels have an important role in coding reward and prediction of reward, in the motivational drive to procure the reward, and in facilitating learning.1 It is also believed that DA codes not just for reward but for saliency, which, in addition to reward, includes aversive, novel, and unexpected stimuli. The diversity of DA effects is likely translated by the specific brain regions (limbic, cortical, and striatal) it modulates. Herein, we summarize findings from imaging studies that used positron emission tomography (PET) to investigate the role of DA in the reinforcing effects of drugs, the long-term brain changes in drug-addicted subjects, and the vulnerability to addiction. Though most of the PET studies on addiction have focused on DA, it is clear that drug-induced adaptations in other neurotransmitters (ie, glutamate, γ-aminobutyric acid, opioids, and cannabinoids) are also involved, but the lack of radioligands has limited their investigation. Role of da on the reinforcing effects of drugs in the human brain The effects of short-term drug exposure on extracellular DA concentrations in the human brain can be studied using PET and D2 DA receptor radioactive ligands that are sensitive to competition with endogenous DA, such as raclopride labeled with carbon 11 (11C). The relationship between the effects of drugs on DA and their reinforcing properties in the human brain (assessed by self-reports of “high” and “euphoria”) was studied for the stimulant drugs methylphenidate and amphetamine. Methylphenidate, like cocaine, increases DA by blocking DA transporters, whereas amphetamine, like methamphetamine, increases DA by releasing it from the terminal via DA transporters. Intravenous methylphenidate (0.5 mg/kg) and amphetamine (0.3 mg/kg) increased the extracellular DA concentration of DA in the striatum, and these increases were associated with increases in self-reports of high and euphoria.2 In contrast, when given orally, methylphenidate (0.75-1 mg/kg) also increased DA but was not perceived as reinforcing.3 Because intravenous administration leads to fast DA changes, whereas oral administration increases DA slowly, the failure to observe the high with oral methylphenidate likely reflects its slow pharmacokinetics. Indeed, the speed at which drugs of abuse enter the brain is recognized as affecting their reinforcing effects.4 This association has also been shown in PET studies that evaluated the pharmacokinetics of cocaine (using [11C]cocaine) and MP (using [11C]methylphenidate) in the human brain, documenting that it was the fast uptake of the drug into the brain but not the brain concentration per se that was associated with getting high.5 The dependency of the reinforcing effects of drugs on brain pharmacokinetic properties suggests a possible association with phasic DA cell firing (fast-burst firing at frequencies >30 Hz), which also leads to fast changes in DA concentration and whose function is to highlight the saliency of stimuli.6 This is in contrast to tonic DA cell firing (slow firing at frequencies around 5 Hz), which maintains baseline steady-state DA levels and whose function is to set the overall responsiveness of the DA system. This led us to speculate that drugs of abuse induce changes in DA concentration that mimic but exceed those produced by phasic DA cell firing. Role of da on the long-term effects of drugs of abuse in the human brain: involvement in addiction Synaptic increases in DA concentration occur during drug intoxication in both addicted and nonaddicted subjects. However, a compulsive drive to continue drug taking when exposed to the drug is not triggered in all subjects. Inasmuch as it is the loss of control and the compulsive drug taking that characterizes addiction, the short-term drug-induced DA level increase alone cannot explain this condition. Because drug addiction requires long-term drug administration, we suggest that in vulnerable individuals (because of genetic, developmental, or environmental factors), addiction is related to the repeated perturbation of DA-regulated brain circuits involved with reward/saliency, motivation/drive, inhibitory control/executive function, and memory/conditioning. Herein, we discuss findings from imaging studies on the nature of these changes. Many radioactive tracers have been used to assess changes in targets involved in DA neurotransmission (Table 1). Using 18-N-methylspiroperidol or [11C]raclopride, we and others have shown that subjects with a wide variety of drug addictions (cocaine, heroin, alcohol, and methamphetamine) have significant reductions in D2 DA receptor availability in the striatum (including the ventral striatum) that persist months after protracted detoxification (reviewed in Volkow et al2). We have also revealed evidence of decreased DA cell activity in cocaine abusers. Specifically, we showed that the striatal increases in DA level induced by intravenous methylphenidate (assessed with [11C]raclopride) in cocaine abusers were substantially blunted when compared with DA level increases in control subjects (50% lower).7 Because DA concentration increases induced by methylphenidate are dependent on DA release, a function of DA cell firing, we speculated that this difference likely reflects decreased DA cell activity in the cocaine abusers. Similar findings have been reported in alcohol abusers.8 These brain-imaging studies suggest 2 abnormalities in addicted subjects that would result in decreased output of DA circuits related to reward; that is, decreases in D2 DA receptors and decreases in DA release in the striatum (including the nucleus accumbens). Each would contribute to the decreased sensitivity in addicted subjects to natural reinforcers. Because drugs are much more potent at stimulating DA-regulated reward circuits than natural reinforcers, we postulated that drugs are still able to activate these down-regulated reward circuits. The decreased sensitivity of reward circuits would lead to decreased interest in day-to-day environmental stimuli, possibly predisposing subjects to seek drug stimulation as a means to temporarily activate these reward circuits underlying the transition from taking drugs to feel high to taking them to feel normal. Preclinical studies have demonstrated a prominent role of DA in motivation that seems to be mediated in part via a DA-regulated circuit involving the orbitofrontal cortex (OFC) and the anterior cingulate gyrus (CG).9 In imaging studies in human subjects using the radioactive tracer fludeoxyglucose F 18, we and others have shown decreased activity in the OFC and CG in different classes of addicted subjects (reviewed in Volkow et al2). Moreover, in both cocaine- and methamphetamine-addicted subjects, we have shown that the reduced activity in the OFC and CG is associated with decreased availability of D2 DA receptors in the striatum (reviewed in Volkow et al7) (Figure). Because the OFC and CG participate in the assignment of value to reinforcers as a function of context, their disruption in the abuser could interfere with their ability to change the saliency value of the drug as a function of alternative reinforcers, becoming the main drive motivating behavior. In contrast to the pattern of decreased OFC and CG activity when drug-free, addicted subjects show increased activation in these regions when presented with the drug or drug-related stimuli, consistent with the enhanced saliency values of drugs or drug reinforcers in these subjects. Moreover, the enhanced activation of the OFC and CG was associated with the intensity of desire for the drug. This has led us to speculate that the hypermetabolism in the OFC and CG triggered by drugs or drug cues underlies the compulsive drug intake, just as it underlies the compulsive behaviors in patients with obsessive-compulsive disorders.10 This dual effect of disruption of the OFC-CG brain circuit is consistent with the behavior of the drug addict, whose compulsion to take the drug overrides competing cognitive-based tendencies not to take the drug; just as in patients with obsessive-compulsive disorders, the compulsion persists despite cognitive attempts to stop the behaviors. The CG and the OFC are also involved with inhibitory control, which led us to postulate that disrupted DA modulation of the OFC and CG also contributes to the loss of control over drug intake by drug-addicted subjects.10 Inhibitory control is also dependent on the dorsolateral prefrontal cortex, which is also affected in addiction (reviewed in Volkow et al2). Abnormalities in the dorsolateral prefrontal cortex are expected to affect processes involved in executive control including impairments in self-monitoring and behavior control, which have an important role in the cognitive changes that perpetuate drug self-administration.10 Circuits underlying memory and learning, including conditioned-incentive learning, habit learning, and declarative memory (reviewed in Vanderschuren and Everitt11), have been proposed to be involved in drug addiction. The effects of drugs on memory systems suggest ways that neutral stimuli can acquire reinforcing properties and motivational salience, that is, through conditioned-incentive learning. In research on relapse, it has been important to understand why drug-addicted subjects experience an intense desire for the drug when exposed to places where they have taken the drug, to persons with whom previous drug use occurred, and to paraphernalia used to administer the drug. This is clinically relevant because exposure to conditioned cues (stimuli associated with the drug) is a key contributor to relapse. Because DA is involved with prediction of reward (reviewed in Schultz9), we hypothesized that DA might underlie conditioned responses that trigger craving. Studies in laboratory animals support this hypothesis: when neutral stimuli are paired with a drug, they will, with repeated associations, acquire the ability to increase DA in the nucleus accumbens and dorsal striatum, becoming conditioned cues. Furthermore, these neurochemical responses are associated with drug-seeking behavior (reviewed in Vanderschuren and Everitt11). In human beings, PET studies with [11C]raclopride recently confirmed this hypothesis by showing that, in cocaine abusers, drug cues (cocaine-cue video of scenes of subjects taking cocaine) substantially increased DA in the dorsal striatum and that these increases were associated with cocaine craving.12,13 Because the dorsal striatum is implicated in habit learning, this association likely reflects the strengthening of habits as chronicity of addiction progresses. This suggests that a basic neurobiologic disruption in addiction might be a DA-triggered conditioned response that results in habits leading to compulsive drug consumption. It is likely that these conditioned responses reflect adaptations in corticostriatal glutamatergic pathways that regulate DA release (reviewed in Vanderschuren and Everitt11). Da and vulnerability to drug abuse A challenging question in the neurobiology of drug abuse is why some individuals are more vulnerable than others to becoming addicted to drugs. Imaging studies suggest that preexisting differences in DA circuits may be one mechanism underlying the variability in responsiveness to drugs of abuse. Specifically, baseline measures of striatal D2 DA receptors in nonaddicted subjects have been shown to predict subjective responses to the reinforcing effects of intravenous methylphenidate treatment; individuals describing the experience as pleasant had substantially lower levels of D2 DA receptors compared with those describing methylphenidate as unpleasant (reviewed in Volkow et al7). This suggests that the relationship between DA levels and reinforcing responses follows an inverted U-shaped curve: too little is not optimal for reinforcement but too much is aversive. Thus, high D2 DA receptor levels could protect against drug self-administration. Support for this was provided by preclinical studies that showed that up-regulation of D2 DA receptors in the nucleus accumbens dramatically reduced alcohol intake in animals previously trained to self-administer alcohol14 and by clinical studies showing that subjects who, despite having a dense family history of alcoholism, were not alcoholics had substantially higher D2 DA receptors in the striatum compared with individuals without such family histories.15 In these subjects, the higher the D2 DA receptors, the higher the metabolism in the OFC and CG. Thus, we postulate that high levels of D2 DA receptors may protect against alcoholism by modulating frontal circuits involved in salience attribution and inhibitory control. Treatment implications Imaging studies have corroborated the role of DA in the reinforcing effects of drugs of abuse in human beings and have extended traditional views of DA involvement in drug addiction. These findings suggest multicomponent strategies for the treatment of drug addiction that include strategies to (1) decrease the reward value of the drug of choice and increase the reward value of nondrug reinforcers, (2) weaken conditioned drug behaviors, (3) weaken the motivational drive to take the drug, and (4) strengthen frontal inhibitory and executive control (Table 2). Back to top Article Information Correspondence: Nora D. Volkow, MD, National Institute on Drug Abuse, 6001 Executive Blvd, Room 5274-MSC 9581, Bethesda, MD 20892 ([email protected]). Accepted for Publication: January 17, 2007. Author Contributions:Study concept and design: Volkow. Acquisition of data: Volkow, Wang, Swanson, and Telang. Analysis and interpretation of data: Volkow, Fowler, Wang, and Telang. Drafting of the manuscript: Volkow and Swanson. Critical revision of the manuscript for important intellectual content: Volkow, Fowler, Wang, Swanson, and Telang. Statistical analysis: Volkow. Obtained funding: Volkow, Fowler, and Wang. Administrative, technical, and material support: Volkow, Fowler, Wang, and Telang. Study supervision: Volkow, Wang, and Telang. Financial Disclosure: None reported. Funding/Support: This study was supported in part by the intramural program of the National Institute on Alcohol Abuse and Alcoholism; grants DA 06891, DA 09490, DA 06278, and AA 09481 from the National Institutes of Health; and the US Department of Energy, Office of Biological and Environmental Research. References 1. Wise RA Brain reward circuitry: insights from unsensed incentives. Neuron 2002;36 (2) 229- 240PubMedGoogle Scholar 2. Volkow NDFowler JSWang GJSwanson JM Dopamine in drug abuse and addiction: results from imaging studies and treatment implications. Mol Psychiatry 2004;9 (6) 557- 569PubMedGoogle Scholar 3. Volkow NDWang GFowler JS et al. Therapeutic doses of oral methylphenidate significantly increase extracellular dopamine in the human brain. J Neurosci 2001;21 (2) RC121PubMedGoogle Scholar 4. Balster RLSchuster CR Fixed-interval schedule of cocaine reinforcement: effects of dose and infusion duration. J Exp Anal Behav 1973;20 (1) 119- 129PubMedGoogle Scholar 5. Volkow NDDing YSFowler JS et al. Is methylphenidate like cocaine? studies on their pharmacokinetics and distribution in human brain. Arch Gen Psychiatry 1995;52 (6) 456- 463PubMedGoogle Scholar 6. Grace AA The tonic/phasic model of dopamine system regulation and its implications for understanding alcohol and psychostimulant craving. Addiction 2000;95S119- S128PubMedGoogle Scholar 7. Volkow NDWang GJFowler JS et al. Decreased striatal dopaminergic responsivity in detoxified cocaine abusers. Nature 1997;386 (6627) 830- 833PubMedGoogle Scholar 8. Martinez DGil RSlifstein M et al Alcohol dependence is associated with blunted dopamine transmission in the ventral striatum [published online ahead of print July 14, 2005]. Biol Psychiatry20055810779786doi:10.10612 /j.biopsych.2005.04.044PubMedGoogle Scholar 9. Schultz W Getting formal with dopamine and reward. Neuron 2002;36 (2) 241- 263PubMedGoogle Scholar 10. Volkow NDFowler JS Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex. Cereb Cortex 2000;10 (3) 318- 325PubMedGoogle Scholar 11. Vanderschuren LJEveritt BJ Behavioral and neural mechanisms of compulsive drug seeking [November 28, 2005]. Eur J Pharmacol20055261-37788doi:10.1016/j.ejphar.2005.09.037PubMedGoogle Scholar 12. Volkow NDWang GJTelang F et al Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J Neurosci2006262465836588 [published correction appears in J Neurosci.2006;26(27):table of contents]. PubMedGoogle Scholar 13. Wong DFKuwabara HSchretlen DJ et al Increased occupancy of dopamine receptors in human striatum during cue-elicited cocaine craving [published online ahead of print September 13, 2006; published correction appears in Neuropsychopharmacology. 2007;32(1):256]. Neuropsychopharmacology2006311227162727doi:10.1038/sj.npp.1301194PubMedGoogle Scholar 14. Thanos PKVolkow NDFreimuth P et al. Overexpression of dopamine D2 receptors reduces alcohol self-administration. J Neurochem 2001;78 (5) 1094- 1103PubMedGoogle Scholar 15. Volkow NDWang GJBegleiter H et al. High levels of dopamine D2 receptors in unaffected members of alcoholic families: possible protective factors. Arch Gen Psychiatry 2006;63 (9) 999- 1008PubMedGoogle Scholar 16. George TPO'Malley SS Current pharmacological treatments for nicotine dependence. Trends Pharmacol Sci 2004;25 (1) 42- 48PubMedGoogle Scholar 17. Tonstad STønnesen PHajek PWilliams KEBilling CBReeves KRVarenicline Phase 3 Study Group, Effect of maintenance therapy with varenicline on smoking cessation: a randomized controlled trial. JAMA 2006;296 (1) 64- 71PubMedGoogle Scholar 18. Krantz MJMehler PS Treating opioid dependence: growing implications for primary care. Arch Intern Med 2004;164 (3) 277- 288PubMedGoogle Scholar 19. Kosten TOwens SM Immunotherapy for the treatment of drug abuse. Pharmacol Ther 2005;108 (1) 76- 85PubMedGoogle Scholar 20. Anton RF Pharmacologic approaches to the management of alcoholism. J Clin Psychiatry 2001;62(suppl 20)11- 17PubMedGoogle Scholar 21. Comer SDSullivan MAYu E et al. Injectable, sustained-release naltrexone for the treatment of opioid dependence: a randomized, placebo-controlled trial. Arch Gen Psychiatry 2006;63 (2) 210- 218PubMedGoogle Scholar 22. King Ade Wit HRiley RCCao DNiaura RHatsukami D Efficacy of naltrexone in smoking cessation: a preliminary study and an examination of sex differences. Nicotine Tob Res 2006;8 (5) 671- 682PubMedGoogle Scholar 23. Maldonado RValverde OBerrendero F Involvement of the endocannabinoid system in drug addiction [published online ahead of print February 17, 2006]. Trends Neurosci2006294225232doi:10.1016/j.tins.2006.01.008PubMedGoogle Scholar 24. Whitworth ABOberbauer HFleischhacker WW et al. Comparison of acamprosate and placebo in long-term treatment of alcohol dependence. Lancet 1996;347 (9013) 1438- 1442PubMedGoogle Scholar 25. Anton RFO'Malley SSCiraulo DA et al. COMBINE Study Research Group, Combined pharmacotherapies and behavioral interventions for alcohol dependence: the COMBINE study; a randomized controlled trial. JAMA 2006;295 (17) 2003- 2017PubMedGoogle Scholar 26. Mardikian PNLarowe SDHedden SKalivas PWMalcolm RJ An open-label trial of N-acetylcysteine for the treatment of cocaine dependence: a pilot study [published online ahead of print November 16, 2006]. Prog Neuropsychopharmacol Biol Psychiatry2007312389394doi:10.1016/j.pnpbp.2006.10.001PubMedGoogle Scholar 27. Johnson BAAit-Daoud NBowden Cl et al. Oral topiramate for treatment of alcohol dependence: a randomized controlled trial. Lancet 2003;361 (9370) 1677- 1685PubMedGoogle Scholar 28. Brodie JDFigueroa ELaska EMDewey SL Safety and efficacy of gamma-vinyl GABA (GVG) for the treatment of methamphetamine and/or cocaine addiction. Synapse 2005;55 (2) 122- 125PubMedGoogle Scholar 29. Kampman KMPettinati HLynch KG et al. A pilot trial of topiramate for the treatment of cocaine dependence. Drug Alcohol Depend 2004;75 (3) 233- 240PubMedGoogle Scholar 30. Shoptaw SYang XRotheram-Fuller EJ et al. Randomized placebo-controlled trial of baclofen for cocaine dependence: preliminary effects for individuals with chronic patterns of cocaine use. J Clin Psychiatry 2003;64 (12) 1440- 1448PubMedGoogle Scholar 31. Dackis CO'Brien C Glutamatergic agents for cocaine dependence. Ann N Y Acad Sci 2003;1003328- 345PubMedGoogle Scholar 32. George TPVessicchio JCTermine AJatlow PIKosten TRO’Malley SS A preliminary placebo-controlled trial of selegiline hydrochloride for smoking cessation. Biol Psychiatry 2003;53 (2) 136- 143PubMedGoogle Scholar

Giuliano, François;Sanchez-Ramos, Antonio;Löchner-Ernst, Dieter;Del Popolo, Giulio;Cruz, Natalio;Leriche, Albert;Lombardi, Giuseppe;Reichert, Susanne;Dahl, Palle;Elion-Mboussa, Albert;Casariego, Joaquin

doi: 10.1001/archneur.64.11.nct70001

Hoppe, Christian;Poepel, Annkathrin;Elger, Christian E.

doi: 10.1001/archneur.64.11.1595pmid: 17998441

Abstract Objective To evaluate the effects of a daily patient reminder on seizure documentation accuracy. Design Randomized controlled trial. Setting Monitoring unit of an academic department of epileptology. Patients Consecutive sample of 91 adult inpatients with focal epilepsies undergoing video-electroencephalographic monitoring. Intervention While all patients were asked to document seizures at the beginning of the monitoring period, patients from the experimental group were reminded each day to document seizures. Main Outcome Measure Documentation accuracy (percentage of documented seizures). Results A total of 582 partial seizures were recorded. Patients failed to document 55.5% of all recorded seizures, 73.2% of complex partial seizures, 26.2% of simple partial seizures, 41.7% of secondarily generalized tonic-clonic seizures, 85.8% of all seizures during sleeping, and 32.0% of all seizures during the awake state. The group medians of individual documentation accuracies for overall seizures, simple partial seizures, complex partial seizures, and secondarily generalized tonic-clonic seizures were 33.3%, 66.7%, 0%, and 83.3%, respectively. Neither the patient reminder nor cognitive performance affected documentation accuracy. A left-sided electroencephalographic focus or lesion, but not the site (frontal or temporal), contributed to documentation failure. Conclusions Patient seizure counts do not provide valid information. Documentation failures result from postictal seizure unawareness, which cannot be avoided by reminders. Unchanged documentation accuracy is a prerequisite for the use of patient seizure counts in clinical trials and has to be demonstrated in a subsample of patients undergoing electroencephalographic monitoring. Seizures are the main symptom of epilepsy and the major target of its treatment. Accordingly, seizure frequency is the primary outcome measure for individual treatment and for clinical trials. Epileptic seizures can be detected objectively by video-electroencephalographic (EEG) monitoring according to international classifications.1 However, because of the high costs, video-based telemetry is only applied in a few patients for a limited period. The adequacy of ambulatory EEG recording is under debate.2-5 According to the present criterion standard, patients are asked to maintain seizure diaries. Thus, modern epileptology, to a large extent, depends on the assumption that patient seizure data provide reliable and valid information. Two studies6,7 confirmed the reliability of patient seizure memory. However, seizure counts are no subjective measure and have to be compared with objective data as derived from EEG monitoring. Former studies8-11 revealed that most patients fail to document about half of their seizures. This study aims at an analysis of the impact of seizure type, vigilance state, side and site of lesion or EEG focus, antiepileptic medication, and cognitive performance on patient seizure documentation failure. Furthermore, to evaluate the role of postictal seizure unawareness vs subsequent documentation failure (eg, because of carelessness), we conducted a randomized controlled trial to test the effects of a daily reminder to document seizures. Methods Study design This was a prospective study with a consecutive sample of adult inpatients in a video-EEG monitoring unit at an academic department of epileptology (single units). A nonblind, randomized controlled trial on the effects of daily reminding patients to document seizures was embedded. The random allocation to the experimental groups (reminder yes vs no) was done weekly (odd or even calendar weeks). Patients All patients referred to the monitoring unit from October 1, 2004, to July 31, 2005, were considered to participate in the study. The inclusion criteria were adult age, diagnosis of epilepsy, recording of at least 1 epileptic seizure during the monitoring period, and written informed consent according to the Declaration of Helsinki. Exclusion criteria were history or recording of pseudoseizures and long-lasting subclinical seizure patterns and generalized epilepsy. The study was restricted to epileptic seizures to exclude unpredictable effects of unclassifiable or psychogenic seizures on seizure documentation. Video-eeg monitoring The EEG examination took place with adhesive electrodes (10-20 system), with additional temporal electrodes. Patients were under permanent video monitoring and were asked to push a warning button to summon the nurse when they felt a seizure coming. In case of seizures, hospital staff came to assist the patient. Ictal and postictal testing of motor, verbal, and memory function was performed. Instructions and measures At the beginning of the monitoring period, all patients were asked to estimate their level of seizure awareness (rating scale: 0% indicates “unaware of all seizures”; and 100%, “recognize all seizures”). All patients received a seizure diary and were asked to carefully document every seizure event. Only the patients from the “reminder” group were reminded every morning to document all seizures during the monitoring period. The number of objective seizures was determined by analysis of the video-EEG monitoring files. Seizure types (simple partial seizures [SPS], complex partial seizures [CPS], secondarily generalized tonic-clonic seizures [sGTCS], and pseudoseizures) and preictal vigilance states (wakefulness or sleep) were classified by an experienced senior neurologist (A.P.) according to international classifications.1,12 To adjust for the varying duration of the monitoring periods, individual seizure frequencies per month were calculated from patient seizure counts and video-EEG data. Seizure documentation accuracy was defined as the percentage of patient-documented seizures. Patients were classified as “perfect documenters” in cases of a 100% rate of documented seizures. Only patients who experienced a respective seizure event were included in group analyses on documentation accuracies for different seizure types and vigilance states. Comprehensive neuropsychological profiles and intelligence-level estimates were available for two-thirds of the patients.13 Statistical analysis Normal distribution was tested by the Kolmogorov-Smirnov goodness-of-fit test. Nonparametric statistical testing was applied when required (Mann-Whitney test, Wilcoxon signed rank test, χ2 test, or Spearman rank correlation). Analysis of variance was applied to explore data for possible interaction effects. All statistical analyses were performed using a commercially available software program (SPSS 12.0G for Windows; SPSS Inc, Chicago, Illinois). Results All eligible patients agreed to participate in the study. Table 1 shows the patient characteristics. Table 2 shows the patient seizure documentation statistics by different seizure types and vigilance states. Of 582 classifiable seizures, 323 were not documented by the patients. The documentation rate clearly depended on preictal vigilance state and seizure type. Seizures occurring during sleep were not documented in 85.8% of all cases in contrast to 32.0% of undocumented seizures in the awake state. The documentation rate for CPS was clearly less than that for SPS, resulting in a different frequency distribution of seizure types depending on how seizures were documented (video-EEG: CPS, 59.6%; SPS, 32.1%; and sGTCS, 8.2%; and patient counts: SPS, 53.3%; CPS, 35.9%; and sGTCS, 10.8%). Of the seizures, 43.6% actually occurred from sleep, while the patient data suggested a far lower portion of 13.9%. Seizure frequencies per month, as projected from the monitoring period, differed significantly depending on whether they were calculated from patient or video-EEG data. Patients activated the push-button alarm ahead of 51 seizures (8.8%) but failed to document 17 (33.3%) of these seizures. The group medians of the individual documentation accuracies are shown in Table 3 (nonnormal distribution Kolmogorov-Smirnov goodness-of-fit test, P < .01 for all measures). More than half of the patients failed to document any CPS (51%), any seizure during sleep (66%), any CPS during sleep (73%), and any SPS during sleep (75%). The rate of perfect documenters is given in Table 3. Perfect documenters experienced more SPS (4.0 vs 0.8; P = .03, Mann-Whitney tests), fewer CPS (1.0 vs 5.6; P < .001), and fewer seizures during sleep (0.7 vs 4.1; P < .001). The patient self-reported seizure awareness was weakly correlated with the percentage of documented CPS (Spearman rank correlation, r = 0.28, P = .02 [n = 71]) and, in a nonsignificant trend, with the overall documentation accuracy (r = 0.20, P = .06). Only 11 of 36 patients who self-reported to be perfectly aware of their seizures actually were perfect documenters. The embedded randomized controlled trial on the effect of a daily reminder failed to reveal group differences in documentation accuracy measures. However, a near-significant difference regarding the group mean percentage of patient-documented SPS from the awake state (n=22), which was lower in the group of regularly reminded patients (reminder group, 49%; and no reminder group, 80%; P = .07, Mann-Whitney test), was revealed. In addition, reminding patients did not affect the rate of perfect documenters in either group (P = .95, χ2 test). This finding indicates that reminding the patient to document seizures is unlikely to improve the documentation accuracy. The seizure documentation accuracy was independent of the number of antiepileptic drugs (AEDs) at the beginning of the monitoring period and of the number of drugs withdrawn during monitoring. Patients with typical add-on drugs, such as lamotrigine (n = 35), levetiracetam (n = 37), or pregabalin (n = 18), received more AEDs at the beginning of the monitoring period, but no other differences were identified (eg, overall seizure count). In patients receiving levetiracetam, but not lamotrigine or pregabalin, the number of CPS during the awake state was significantly increased (1.9 vs 1.4; P = .048, Mann-Whitney test). Furthermore, the percentage of documented overall CPS and CPS from the awake state was significantly higher in levetiracetam-treated patients than in patients receiving other medication (CPS, 47% vs 27% [P = .04]; CPS from the awake state, 60% vs 35% [P = .048]; Mann-Whitney tests), but documentation accuracy was unchanged in 4 patients with levetiracetam withdrawal during the monitoring period. Because levetiracetam was not experimentally controlled for, this finding may be because of a random sample effect. However, it may also indicate a differential impact of AEDs on seizure awareness (eg, by affecting seizure types or shifting seizures to other vigilance states). The accuracy of seizure documentation was not correlated with neuropsychological performance, including verbal or nonverbal memory, verbal fluency, and intelligence level, nor was it correlated with academic achievement. However, in a group comparison, perfect seizure documenters had better verbal memory performance (delayed free recall, group mean ± SD t score, 44 ± 9 vs 40 ± 10; P = .04, Mann-Whitney test) and higher school education (advanced level: 23 of 35 vs 23 of 56; χ21 = 5.2, P = .02) than did nonperfect documenters. No effects of age, sex, or duration of epilepsy on overall documentation accuracy were obtained. However, the rate of documented sGTCS, but not of other types, was correlated with age at seizure onset (r = 0.47, P = .01 [n = 27]), indicating more accurate documentation in patients who were older at seizure onset. Table 4 shows the data separately for patients with left- and right-sided frontal and temporal lobe epilepsy. No main effect of the site of lesion or EEG focus (temporal vs frontal) was obtained. The group mean documentation accuracy was slightly, but not significantly, lower in patients with left-sided than in patients with right-sided lesions or EEG focus (left vs right, 45% vs 67%; P=.06, Mann-Whitney test); this group difference was significant for seizures from sleeping (left vs right, 26% vs 71%; P = .04). Of 20 patients with right-sided temporal or frontal lobe epilepsy, 13 (65.0%) were perfect documenters, vs 13 of 39 patients (33.3%) with left-sided temporal or frontal lobe epilepsy (χ21 = 5.4, P = .02) (nonclassified data: P = .06, Mann-Whitney test). The effects of the factor site (temporal vs frontal lobe) and side (left vs right) on documentation accuracy were tested by an explorative 2-factorial analysis of variance that revealed a main effect for side but not for site and no interaction effect. No effect was obtained when the side of the epileptic focus was defined relatively to the speech-dominant hemisphere (Wada test, 15 patients; functional magnetic resonance imaging, 2 patients; results: 10 left sided, 5 bilateral, and 2 right dominant). This finding indicates a role of left hemisphere functional disturbance for seizure unawareness that may be more pronounced if the left frontal lobe is involved. Comment Former studies8-10 and the findings of the present study give evidence that patient seizure counts do not reflect the objective seizure and risk burden. Only a few patients (38.5%) were able to document all seizures accurately, whereas most of the patients fail to document the major part of their seizures (total rate of undocumented seizures, 55%). The low documentation accuracy is unlikely to result from the specific conditions of video-EEG monitoring (eg, lying in bed)8,14 because under ambulatory conditions quite similar findings were obtained.11 Underreporting may be a misleading term because it implies that patients could have reported the correct figures if they had put more effort into it. However, the embedded randomized controlled trial on the effects of a patient reminder could not reveal any evidence that urging patients to document and supporting their memory increases documentation accuracy. No effect of cognitive performance on documentation accuracy was obtained. Finally, former studies6,7 already demonstrated the reliability of patient seizure memory. Thus, it is unlikely that patients are careless or forget to document. Underreporting is, rather, caused by processes that are out of the patient's control. For example, seizures occurring during sleeping (85.8%) and CPS (73.2%) are at the highest risk of not being documented. Seizure-induced seizure unawareness is a frequent, but rather unrecognized, postictal symptom particularly associated with seizures from sleeping and with CPS. The mechanisms underlying seizure unawareness are not yet clear. In our sample, besides the vigilance state and the seizure type, the left-sided focus or lesion, but not the site, of the epileptic focus or lesion (temporal or frontal lobe) contributed moderately to seizure unawareness, which is in accord with former studies.9,10 In contrast to the study by Blum et al,8 sGTCS were recognized and documented even more often than CPS. From neurocognitive studies15,16 of the ictal state and from the definition of CPS, including impaired consciousness, it is clear that the patient is dependent on unambiguous postictal bodily signs (eg, muscle pain, tongue bite, or enuresis), environmental changes (eg, broken glass), or social reactions (eg, caring proxies) to become aware that a seizure had occurred. Our study revealed a possible interaction of seizure awareness with anticonvulsant medication, such that patients receiving levetiracetam had better documentation accuracy than did patients receiving another AED. This explorative finding may result from a random sample effect but may also indicate specific effects of AEDs on seizure awareness and documentation accuracy. However, true seizure efficacy can be calculated from patient data only under the assumption that documentation accuracy is unaffected by the treatment. This study was restricted to adult patients with partial seizures. Findings and conclusions may not be applicable to patients with primarily generalized seizures or to pediatric patients under all-day observation. Furthermore, most of our patients underwent video-EEG monitoring for presurgical workup, which may also have biased the pattern of results. In conclusion, patient seizure counts are not valid and reports of complete seizure freedom may need objective evaluation (eg, regarding a driver's license). Seizure underreporting is a consequence of postictal seizure unawareness, rather than of careless documentation. Reminding the patient to document seizures will, therefore, not improve documentation accuracy. For premarket evaluation of new treatments for partial seizures (drugs and devices), additional EEG-based seizure data are required. An unambiguous demonstration of unchanged seizure awareness (documentation accuracy) under the new treatment based on EEG monitoring is a prerequisite of calculating valid seizure frequency reduction (percentage) from (invalid) patient seizure counts. Alternative designs for clinical trials, including video-EEG monitoring, have been proposed by Bien and Elger.17 Back to top Article Information Correspondence: Christian Hoppe, PhD, Department of Epileptology, University of Bonn Medical Centre, Sigmund-Freud-Strasse 25, FRG-53105 Bonn, Germany ([email protected]). Accepted for Publication: December 5, 2006. Author Contributions: Drs Hoppe and Poepel contributed equally to this work. Study concept and design: Hoppe, Poepel, and Elger. Acquisition of data: Poepel. Analysis and interpretation of data: Hoppe and Poepel. Drafting of the manuscript: Hoppe and Poepel. Critical revision of the manuscript for important intellectual content: Hoppe, Poepel, and Elger. Statistical analysis: Hoppe and Poepel. Administrative, technical, and material support: Poepel and Elger. Study supervision: Hoppe. Financial Disclosure: None reported. References 1. Commission on Classification and Terminology of the International League Against Epilepsy, Proposal for revised classification of epilepsies and epileptic syndromes. Epilepsia 1989;30 (4) 389- 399PubMedGoogle ScholarCrossref 2. Cascino GD Video-EEG monitoring in adults. Epilepsia 2002;43(suppl 3)80- 93PubMedGoogle ScholarCrossref 3. Chang BSIves JRSchomer DLDrislane FW Outpatient EEG monitoring in the presurgical evaluation of patients with refractory temporal lobe epilepsy. J Clin Neurophysiol 2002;19 (2) 152- 156PubMedGoogle ScholarCrossref 4. Ghougassian DFd’Souza WCook MJO’Brien TJ Evaluating the utility of inpatient video-EEG monitoring. Epilepsia 2004;45 (8) 928- 932PubMedGoogle ScholarCrossref 5. Gilliam FKuzniecky RFaught E Ambulatory EEG monitoring. J Clin Neurophysiol 1999;16 (2) 111- 115PubMedGoogle ScholarCrossref 6. Neugebauer R Reliability of seizure diaries in adult epileptic patients. Neuroepidemiology 1989;8 (5) 228- 233PubMedGoogle ScholarCrossref 7. Glueckauf RLGirvin JPBraun JRBochen JL Consistency of seizure frequency estimates across time, methods, and observers. Health Psychol 1990;9 (4) 427- 434PubMedGoogle ScholarCrossref 8. Blum DEEskola JBortz JJFisher RS Patient awareness of seizures. Neurology 1996;47 (1) 260- 264PubMedGoogle ScholarCrossref 9. Inoue YMihara T Awareness and responsiveness during partial seizures. Epilepsia 1998;39(suppl 5)7- 10PubMedGoogle ScholarCrossref 10. Kerling FMueller SPauli EStefan H When do patients forget their seizures? an electroclinical study. Epilepsy Behav 2006;9 (2) 281- 285PubMedGoogle ScholarCrossref 11. Tatum WO IVWinters LGieron M et al. Outpatient seizure identification: results of 502 patients using computer-assisted ambulatory EEG. J Clin Neurophysiol 2001;18 (1) 14- 19PubMedGoogle ScholarCrossref 12. Rechtschaffen AKales A A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Washington, DC: Public Health Service, US Government Printing Office; 1968 13. Helmstaedter CElger CE Cognitive consequences of two-thirds anterior temporal lobectomy on verbal memory in 144 patients: a three-month follow-up study. Epilepsia 1996;37 (2) 171- 180PubMedGoogle ScholarCrossref 14. Eisenman LNAttarian HFessler AJVahle VJGilliam F Self-reported seizure frequency and time to first event in the seizure monitoring unit. Epilepsia 2005;46 (5) 664- 668PubMedGoogle ScholarCrossref 15. Jokeit HDaamen MZang HJanszky JEbner A Seizures accelerate forgetting in patients with left-sided temporal lobe epilepsy. Neurology 2001;57 (1) 125- 126PubMedGoogle ScholarCrossref 16. Bell WLPark YDThompson EARadtke RA Ictal cognitive assessment of partial seizures and pseudoseizures. Arch Neurol 1998;55 (11) 1456- 1459PubMedGoogle ScholarCrossref 17. Bien CGElger CE Monotherapy trials in antiepileptic drugs: are modified “presurgical studies” a way out of the dilemma? Epilepsy Res 2001;44 (1) 1- 5PubMedGoogle ScholarCrossref

Grossman, Murray;Libon, David J.;Forman, Mark S.;Massimo, Lauren;Wood, Elisabeth;Moore, Peachie;Anderson, Chivon;Farmer, Jennifer;Chatterjee, Anjan;Clark, Christopher M.;Coslett, H. Branch;Hurtig, Howard I.;Lee, Virginia M.-Y.;Trojanowski, John Q.

Hu, William T.;Mandrekar, Jayawant N.;Parisi, Joseph E.;Knopman, David S.;Boeve, Bradley F.;Petersen, Ronald C.;Hutton, Michael;Dickson, Dennis W.;Josephs, Keith A.

doi: 10.1001/archneur.64.11.1611pmid: 17998443

Abstract Objective To identify clinical features in behavioral-variant frontotemporal dementia that may help predict tau-positive pathology. Methods Clinical and historical features of patients with pathologically confirmed tau-positive and tau-negative frontotemporal lobar degeneration from 1970 to 2006 were retrospectively reviewed in a blinded fashion. The initial clinical features of those patients who eventually met consensus criteria for frontotemporal dementia were examined using univariate and cluster analyses to explore characteristics that may be associated with tau pathology. Results Fifty-six patients (24 tau-positive cases) were included in the analysis. There was no difference in demographics between the tau-positive and tau-negative cases. Univariate analysis showed that poor planning and/or judgment was more commonly associated with tau-positive pathology (P = .03). Cluster analysis using behavioral characteristics identified 2 groups of patients: cluster 1 contained mainly tau-positive cases (57%) and cluster 2 was mostly tau-negative cases (71%). Poor planning and/or judgment was a common presenting sign in the first group (P < .001), while the second group was more likely to present with impaired regulation of personal conduct (P < .001) and a decline in personal hygiene (P = .005). Conclusions Poor planning and/or judgment was associated with behavioral-variant frontotemporal dementia patients who had tau-positive pathology. The constellation of impaired personal conduct and a paucity of dysexecutive symptoms identified tau-negative patients. The syndrome of frontotemporal dementia (FTD) describes a clinical spectrum of progressive cognitive impairment that shares pathologic features with frontotemporal lobar degeneration (FTLD).1 Patients with FTD can present with deficits in behavior, language, or both.2 Pathologic changes associated with the behavioral variant of FTD (bvFTD)3 can be divided into tau-negative and tau-positive neurodegenerative diseases. Frontotemporal lobar degeneration with ubiquitin-positive and tau- and α-synuclein–negative inclusions (FTLD-U) and FTLD with motor neuron degeneration (FTLD-MND) form a group of tau-negative degeneration.4-6 Pick disease, multiple system tauopathy, and FTD with parkinsonism linked to chromosome 17 (FTDP-17) are tau-positive FTLDs that can present with bvFTD, though cases of tau-positive corticobasal degeneration and progressive supranuclear palsy can also present with a similar clinical syndrome.7 Personality changes, decline in personal and interpersonal conduct, and dysexecutive features are common presenting symptoms of bvFTD,1 but it is not well understood whether select features are preferentially associated with tau pathology.7 With the recent discovery of mutations in the progranulin gene (PGRN) associated with FTLD-U,8 it is also unclear whether the mutations result in a distinct clinical phenotype in the FTD spectrum.9 Early identification of tau pathology has significant prognostic implications and is important in identifying patients suitable for substrate-specific therapy when it becomes available. In our recent review7 of a published autopsy-confirmed series of FTLD that presented clinically with bvFTD, there was an equal proportion of tau-positive and tau-negative cases. Because underlying tau pathology was strongly associated with specific clinical phenotypes in a language variant of FTD,6,7 we hypothesize that certain behavioral characteristics of bvFTD may be associated with tau-positive pathology. Hence, we reviewed the historical information and symptoms of patients from their initial clinical evaluation with autopsy-confirmed FTLD and present common and distinguishing features. Methods The Mayo Clinic's (Rochester, Minnesota) autopsy database was searched to identify all cases that had a pathologic diagnosis of FTLD-U, FTLD-MND, Pick disease, FTDP-17, multiple system tauopathy, corticobasal degeneration, or progressive supranuclear palsy between January 1, 1970, and December 31, 2006. Neuropathologists (J.E.P. and D.W.D.) who were experienced in degenerative neuropathology pathologically reexamined all cases with modern techniques, as described elsewhere.7,10 Briefly, slides of the frontal, temporal, and parietal neocortex; hippocampus; basal ganglia; thalamus; midbrain; pons; medulla; and cerebellum were reviewed. In all cases, sections were studied using hematoxylin-eosin and modified Bielschowsky staining as well as other stains needed for routine evaluation, including immunohistochemistry for markers of glial pathology. Those stains include glial fibrillar acid protein for astrocytes and either CD68 or HLA-DR antigens for microglia. Neuronal pathology was studied with antibodies to neurofilament protein, ubiquitin, α-synuclein, and phospho-tau. Cases originally designated as dementia lacking distinct histopathology were previously reexamined and reclassified according to current histopathologic techniques.7 Pick disease was diagnosed if round silver and tau-positive neuronal inclusion bodies were found in the frontotemporal cortex and other subcortical nuclei11; multiple system tauopathy was diagnosed by widespread tau-positive globular, neuronal, and glial inclusions and a negative microtubule-associated protein tau screening; and FTDP-17 was diagnosed if there was a mutation found in microtubule-associated protein tau sequencing. Corticobasal degeneration was diagnosed if neurofilament-positive ballooned neurons and tau-positive coiled bodies, threads, and astrocytic plaques affecting cardinal nuclei were found. Progressive supranuclear palsy was diagnosed if tau-positive globose neurofibrillar tangles, coiled bodies, threads, and tufted astrocytes affecting cardinal nuclei were found. Diagnosis of FTLD-U was made if there was frontal and/or temporal lobe neurodegenerative changes plus ubiquitin-positive, tau-, alpha-synuclein–, and neurofilament-negative abnormal neurites or neuronal inclusions in the frontotemporal cortex, or a dentate granule cell layer of the hippocampus and an absence of histologic evidence of MND.4,12 A diagnosis of FTLD-MND was made if there was FTLD and MND or degeneration of the corticospinal tract. Bunina bodies were sought in all cases and determined as helpful in making a diagnosis of FTLD-MND; but alone, they were not sufficient to make this diagnosis. Once the cases were identified through searching the existing database, patients with clinical presentation of language-variant FTD were excluded.13 K.A.J. previously reviewed all records throughout the disease course to ensure that the patient met or eventually met consensus criteria for bvFTD,1 even though at the time of initial clinical evaluation (1970-2006), the diagnosis recorded may not have been FTD.7 K.A.J. then removed the final pathologic diagnosis from patient records. Cases were included in this study if a behavioral neurologist evaluated patients who had sufficiently detailed history and there was no coexisting pathology that could account for some of the clinical symptoms. W.T.H. then retrospectively reviewed historical records from the initial clinical visits of all cases in a blinded fashion. Clinical information on initial neurological evaluation was then abstracted. Symptoms were considered present if they constituted significant complaints by patients and family members. Clinical signs were considered present only if a behavioral neurologist definitively recognized them and they could not be explained by another etiology. Behavioral features were considered absent if they were explicitly stated as such or their presence was not mentioned. Because a behavioral neurologist with expertise in FTD examined all patients, the inclusion of symptoms in this study required the symptom to be of at least a mild to moderate severity. We paid special attention to clinical characteristics outlined in the consensus diagnostic criteria of bvFTD, including core features, supportive features, and exclusion criteria. These include personality and behavior change, decline in social interpersonal conduct, impaired regulation of personal conduct, emotional blunting, and poor planning and/or judgment (including poor organization and poor problem solving in daily activities or at work); loss of insight, decline in personal hygiene and grooming, hyperorality, dietary change, perseverative and/or repetitive behavior, loss of empathy, speech and language dysfunction, and incontinence; motor symptoms including parkinsonism, spasticity, or myoclonus; and delusions and/or paranoia, forgetfulness and/or amnesia, topographical disorientation, and deficits in facial recognition. Some of these patients may have had minor symptoms related to previous psychiatric or other comorbid illnesses; thus, consideration was given to dominant symptoms.6 An additional feature of hypersomnolence was included, as a number of patients' family members shared a complaint that the patients slept all day. Features of mental rigidity and inflexibility, distractibility and impersistence, utilization behavior, and logoclonic speech were not found in the patients' records and were therefore not included in the statistical analysis. Primitive reflexes were also excluded as they were inconsistently documented. Additional information that was abstracted included patients' age at onset, age at initial evaluation, duration of disease (time from symptomatic onset to death), sex, family history of dementia (including FTD and other nonstroke-related cognitive impairment) and/or MND, and any abnormality on cerebral spinal fluid examination. PGRN mutation status was available for 17 patients with a pathologic diagnosis of FTLD-U, as described elsewhere.8 Statistical analyses were performed using JMP computer software, version 6.0.0 (SAS Institute Inc, Cary, North Carolina), with statistical significance set at P < .05. For univariate analysis, χ² or Fisher exact tests were used for dichotomous variables and the t test was used for continuous variables. For cluster analysis, only behavioral features (excluding pathologic diagnosis, age, sex, and family history) that were present in more than 20% of all patients were included. Behavioral characteristics found to distinguish features between newly generated clusters were then added to the list of behavioral features if they were initially excluded. Using this method, a hierarchical cluster analysis of the cases was performed using only the behavioral variables of interest (personality and behavioral change, decline in social interpersonal conduct, impaired regulation of personal conduct, preservative and/or repetitive behavior, poor planning and/or judgment, decline in personal hygiene, delusions and/or paranoia, hypersomnolence, and motor symptoms including parkinsonism). A clustering procedure from SAS, version 8.0 (SAS Institute Inc), which identified hierarchical clusters of observations from the data set of small sample sizes, was used for the analysis. The Ward minimum-variance method,14 in which the distance between 2 clusters was the analysis of variance sum of squares between the 2 clusters added across all the variables, was used. At each generation, the within-cluster sum of squares was minimized across all partitions obtainable by merging 2 clusters from the previous generation. The sums of squares were then divided by the total sum of squares to give proportions of variance (squared semipartial correlations) for ease of interpretation. Pseudo F statistic and pseudo t2 statistic were primarily used to judge the number of clusters appropriate for this data set. Squared multiple correlation, which was the proportion of variance accounted for by the clusters, was also estimated. Results A total of 66 cases of tau-positive and tau-negative neurodegenerative diseases were identified from the Mayo Clinic's autopsy database. Ten cases were excluded owing to insufficient history (4 FTLD-U, 2 FTLD-MND, and 2 Pick disease cases), lack of evaluation by a behavioral neurologist (1 Pick disease case), and concurrent demyelinating pathology (1 FTLD-U case). Thus, 32 tau-negative cases (26 FTLD-U and 6 FTLD-MND cases) and 24 tau-positive cases (10 Pick disease, 4 FTDP-17, 1 multiple system tauopathy, 5 corticobasal degeneration, and 4 progressive supranuclear palsy cases) were included in this study. Five of the tau-negative cases had PGRN mutations (all FTLD-U). The median time from symptomatic onset to clinical evaluation was 3 years for tau-positive cases and 2 years for tau-negative cases. There was no statistically significant difference in sex, age at onset, age at evaluation, or age at death. Patients with tau-positive pathology tended to have longer disease duration than patients with tau-negative pathology (P = .07). Among disease subtypes, patients with FTLD-MND had the shortest disease duration (median survival, 3 years vs 8 years for FTLD-U and 8 years for tau-positive FTLD cases; P < .001), which reflected the previously observed trend.10 Univariate analysis revealed poor planning and/or judgment to be more common among cases with tau-positive pathology (P = .03) (Table 1) and family history to be more common among tau-negative cases. In addition, cases with tau-negative pathology were more likely to have delusions and/or paranoia or an initial clinical diagnosis of FTD. Among cases not given an initial diagnosis of FTD, 1 tau-positive case and 1 tau-negative case were diagnosed as corticobasal degeneration syndrome; 1 tau-positive case was diagnosed as parkinsonism-plus syndrome; and 1 tau-negative case was diagnosed as clinically possible Alzheimer disease with parkinsonism. Other cases were either diagnosed as clinically possible Alzheimer disease (2 tau-positive and 3 tau-negative cases), mild cognitive impairment (1 tau-positive case), or dementia (4 tau-positive cases). Cluster analysis using 9 behavioral variables (personality and behavioral change, decline in social interpersonal conduct, impaired regulation of personal conduct, preservative and/or repetitive behavior, poor planning and/or judgment, decline in personal hygiene, delusion and/or paranoia, hypersomnolence, and motor symptoms including parkinsonism) generated 2 clusters of patients (Figure). Compared with a smaller or larger number of clusters, having 2 clusters via the Ward minimum-variance method gave a relatively large pseudo F statistic of 11.6, indicating that an appropriate number of clusters was generated. This was confirmed by the pseudo t2 statistic of 8.4, which was markedly larger than values generated from fewer or more clusters. The dendrogram generated from this clustering (Figure) showed the proportion of variance accounted by the clustering to be about 18%. With the clinical information available, this was the most preferred clustering strategy. Patients in cluster 1 and cluster 2 were distinct clinically and pathologically (Table 2). Clinically, patients in cluster 1 were more likely to have poor planning and/or judgment (P < .001), and patients in cluster 2 were more likely to have impaired regulation of personal conduct (P < .001) and decline in personal hygiene (P = .005). Pathologically, tau-positive cases were more common in cluster 1, which contained the majority of Pick disease (8 of 10) and corticobasal degeneration (4 of 5) cases, but this only approached statistical significance (P = .052). On the other hand, 20 of 28 patients (71%) in cluster 2 were tau negative. A search of the original group of 56 patients with results from the cluster analysis showed that constellation of impaired regulation of conduct and absence of prominent planning and/or judgment difficulties identified a group of 22 patients, 77% of which were tau-negative, which represented 62% of all tau-negative cases in this study. As a secondary observation, we noted that 4 of the 12 tau-negative cases in cluster 1 had PGRN mutations, while only 1 of the 20 tau-negative cases in cluster 2 had a PGRN mutation. After excluding patients with PGRN mutations, 67% of patients in cluster 1 had a tau-positive neurodegenerative disease. Comment With the development of the consensus diagnostic criteria,1 FTLD can be diagnosed clinically with high specificity and relatively high sensitivity.15 However, little is known about the association between clinical features and FTLD subtypes. In this large group of bvFTD patients with autopsy-confirmed FTLD, we observed 2 behavioral groups with most cases within each being either tau-positive (cluster 1) or tau-negative (cluster 2). Thus, specific behavioral phenotypes are likely associated with FTLD subtypes, though predicting tauopathy with behavioral phenotype alone lacks high specificity. Clustering of clinical characteristics was previously used to divide the language variant of FTD into fluent and nonfluent variants with high pathologic concordance.16 Knibb et al16 demonstrated that 53% of the fluent group had ubiquitin-associated pathologic changes and that 43% of the nonfluent language group had non–Alzheimer disease tauopathies. Using a similar technique, behavioral stratification of bvFTD cases in our series created 2 behaviorally distinct groups. Patients with tau-positive pathology, who were characterized behaviorally by the occurrence of early dysexecutive symptoms of poor planning and/or judgment, were more likely to be found in cluster 1. This corresponds with previous observations that tau-positive cases of FTLD have relatively more severe atrophy in the bilateral prefrontal regions.17 However, cluster analysis did not significantly improve the predictive value for tau pathology. Interestingly, a significant percentage of tau-negative cases in cluster 1 were FTLD-U cases with PGRN mutations, raising the possibility that tau-positive diseases (the largest subgroup being Pick disease) and FTLD-U with PGRN mutations share a common clinical phenotype. One explanation may be a shared pattern of lobar degeneration in tau-positive cases and FTLD-U with PGRN mutations. In 2 separate series, patients with Pick disease mutations were shown to have a greater degree of frontal lobe atrophy on radiographic studies when compared with other FTLD subtypes.18,19 At the same time, cases of FTLD-U with PGRN mutations also demonstrated more frontal lobe atrophy than cases of FTLD-U without PGRN mutations on pathologic studies.9 Therefore, in patients who display behavioral characteristics that are common in cluster 1 or significant frontal lobar atrophy, PGRN mutation screening will be useful in refining the predictive model of tau positivity. Cases with PGRN mutations may be suitable for PGRN-specific therapies in the future, and cases without PGRN mutations will have a high likelihood of being tau positive. We also noted that previous series reported higher prevalence of executive dysfunction in clinically diagnosed cases of FTD. One study reported that more than 70% of patients with clinically diagnosed bvFTD showed symptoms of poor planning and lack of judgement.3 Another showed a high prevalence (87%) of dysexecutive features in cases of familial tau mutation–negative cases of FTD.20 This may reflect a different proportion of tau-positive cases based on findings from our study, as 2 large clinicopathologic series have previously demonstrated higher proportions of tau-positive cases (46 of 76 cases5 and 31 of 61 cases6). Similarly, symptoms of FTD may increase in severity over time, and a higher prevalence of symptoms may reflect clinical evaluations later in the disease course. As there was minimal difference in the median time to evaluation from symptom onset between tau-positive and tau-negative cases, the higher observed tendency of patients with tau-positive disease to have poor planning and/or judgment early in the disease course in our series likely cannot be explained by disease duration alone. However, there remains the possibility that tau-positive and tau-negative cases will have similar rates of planning and/or judgment deficits late in the disease course. With the available pathologic and genetic information in our series, we propose that patterns of early abnormal behaviors associated with tau-positive FTLD may increase the clinical diagnostic accuracy of FTD subtypes in conjunction with other techniques, such as volumetric imaging and PGRN mutational studies.19,21 This study has a number of limitations. This was a retrospective study and we did not adjust for the multiple comparisons problem owing to the overall sample size, though the very small allowable errors in the univariate comparison following cluster analysis for poor planning and/or judgment and impaired regulation of personal conduct support significant differences between the clusters. The extent of clinical documentation may have varied even among expert behavioral neurologists, which could have been reflected in the underreporting of nonbehavioral symptoms. Importantly, we recognize that absence of documentation likely may not always reflect absence of symptoms, though the recording of symptoms not thought to be common to FTD, such as hypersomnolence, would support the notion that even mild symptoms were indeed documented. At the same time, there was a high degree of concordance in the pattern of clinical features noted with previous reports. Tau-negative FTLD-U cases were more likely to display signs of social or behavioral changes than tau-positive cases including Pick disease in one study,22 and only half of patients with tau-positive Pick disease had FTD as their first clinical syndrome in another study.23 However, to definitively confirm the trends observed in this and previous studies, the dominant clinical phenotypes identified here could form the basis for prospective clinicopathologic studies to determine the utility of behavioral phenotypes in predicting tau-positive neurodegenerative diseases, with standardized neuropsychometric batteries to better assess severity of common symptoms and special attention to focus on less common but perhaps characteristic symptoms, such as delusions/paranoia and decline in personal hygiene. We present a clinicopathologic study of FTLD subtypes and found that poor planning and/or judgment is more prevalent among patients with tau-positive pathology and that impaired regulation of personal conduct and delusion and/or paranoia is more commonly associated with tau-negative cases. In conjunction with imaging and PGRN mutational screening, these findings may help predict FTLD. However, overlap of clinical syndromes and potential conversion between FTD subtypes may complicate the interpretation of early behavioral deficits. Long-term prospective clinicopathologic studies are necessary to determine the predictive values of subcategorizing bvFTD based on specific behavioral features. Biomarkers for tauopathies and ubiquitinopathies are needed to reliably differentiate these cases in the clinical setting. Back to top Article Information Correspondence: Keith A. Josephs, MST, MD, Department of Neurology, Behavioral Neurology and Movement Disorders, 200 1st St SW, Rochester MN 55905 ([email protected]). Submitted for Publication: December 13, 2006; final revision received March 22, 2007; accepted March 22, 2007. Author Contributions: Drs Hu and Josephs had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Hu and Josephs. Acquisition of data: Hu, Parisi, Boeve, Petersen, Hutton, Dickson, and Josephs. Analysis and interpretation of data: Hu, Mandrekar, Parisi, Knopman, and Josephs. Drafting of the manuscript: Hu and Josephs. Critical revision of the manuscript for important intellectual content: Hu, Mandrekar, Parisi, Knopman, Boeve, Petersen, Hutton, Dickson, and Josephs. Statistical analysis: Mandrekar and Josephs. Administrative, technical, and material support: Dickson and Josephs. Study supervision: Boeve, Petersen, Hutton, and Josephs. Financial Disclosure: None reported. Funding/Support: This study was supported by grant (K12/NICHD)-HD49078 from the National Institutes of Health Roadmap Multidisciplinary Clinical Research Career Development Program (Dr Josephs), grants P50 AG16574 and U01 AG06786 from the National Institute on Aging, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer's Disease Research Program of the Mayo Foundation. References 1. Neary DSnowden JSGustafson L et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 1998;51 (6) 1546- 1554PubMedGoogle ScholarCrossref 2. McKhann GMAlbert MSGrossman MMiller BDickson DTrojanowski JQ Clinical and pathological diagnosis of frontotemporal dementia: report of the Work Group on Frontotemporal Dementia and Pick's Disease. Arch Neurol 2001;58 (11) 1803- 1809PubMedGoogle ScholarCrossref 3. Bozeat SGregory CARalph MAHodges JR Which neuropsychiatric and behavioural features distinguish frontal and temporal variants of frontotemporal dementia from Alzheimer's disease? J Neurol Neurosurg Psychiatry 2000;69 (2) 178- 186PubMedGoogle ScholarCrossref 4. Josephs KAHolton JLRossor MN et al. Frontotemporal lobar degeneration and ubiquitin immunohistochemistry. Neuropathol Appl Neurobiol 2004;30 (4) 369- 373PubMedGoogle ScholarCrossref 5. Lipton AMWhite CL IIIBigio EH Frontotemporal lobar degeneration with motor neuron disease-type inclusions predominates in 76 cases of frontotemporal degeneration. Acta Neuropathol (Berl) 2004;108 (5) 379- 385PubMedGoogle ScholarCrossref 6. Hodges JRDavies RRXuereb JH et al. Clinicopathological correlates in frontotemporal dementia. Ann Neurol 2004;56 (3) 399- 406PubMedGoogle ScholarCrossref 7. Josephs KAPetersen RCKnopman DS et al. Clinicopathologic analysis of frontotemporal and corticobasal degenerations and PSP. Neurology 2006;66 (1) 41- 48PubMedGoogle ScholarCrossref 8. Baker MMackenzie IRPickering-Brown SM et al. Mutations in progranulin cause tau-negative frontotemporal dementia linked to chromosome 17. Nature 2006;442 (7105) 916- 919PubMedGoogle ScholarCrossref 9. Josephs KAAhmed ZKatsuse O et al. Neuropathologic features of frontotemporal lobar degeneration with ubiquitin-positive inclusions with progranulin gene (PGRN) mutations. J Neuropathol Exp Neurol 2007;66 (2) 142- 151PubMedGoogle ScholarCrossref 10. Josephs KAKnopman DSWhitwell JL et al. Survival in two variants of tau-negative frontotemporal lobar degeneration: FTLD-U vs FTLD-MND. Neurology 2005;65 (4) 645- 647PubMedGoogle ScholarCrossref 11. Dickson DW Neuropathology of Pick's disease. Neurology 2001;56 (11) (suppl 4)S16- S20PubMedGoogle ScholarCrossref 12. Lowe JRossor MN Frontotemporal lobar degeneration. In: Dickson D, ed. Neurodegeneration: The Molecular Pathology of Dementia and Movement Disorders. Basel, Switzerland: ISN Neuropath Press; 2003:342-348Google Scholar 13. Josephs KADuffy JRStrand EA et al. Clinicopathological and imaging correlates of progressive aphasia and apraxia of speech. Brain 2006;129 (pt 6) 1385- 1398PubMedGoogle ScholarCrossref 14. Ward JH Hierarchical grouping to optimize an objective function. J Am Stat Assoc 1963;58 (301) 236- 244Google ScholarCrossref 15. Knopman DSBoeve BFParisi JE et al. Antemortem diagnosis of frontotemporal lobar degeneration. Ann Neurol 2005;57 (4) 480- 488PubMedGoogle ScholarCrossref 16. Knibb JAXuereb JHPatterson KHodges JR Clinical and pathological characterization of progressive aphasia. Ann Neurol 2006;59 (1) 156- 165PubMedGoogle ScholarCrossref 17. Whitwell JLWarren JDJosephs KA et al. Voxel-based morphometry in tau-positive and tau-negative frontotemporal lobar degenerations. Neurodegener Dis 2004;1 (4-5) 225- 230PubMedGoogle ScholarCrossref 18. Whitwell JLJosephs KARossor MN et al. Magnetic resonance imaging signatures of tissue pathology in frontotemporal dementia. Arch Neurol 2005;62 (9) 1402- 1408PubMedGoogle ScholarCrossref 19. Whitwell JLJack CR JrBaker M et al. Voxel-based morphometry in frontotemporal lobar degeneration with ubiquitin-positive inclusions with and without progranulin mutations. Arch Neurol 2007;64 (3) 371- 376PubMedGoogle ScholarCrossref 20. Piguet OBrooks WSHalliday GM et al. Similar early clinical presentations in familial and non-familial frontotemporal dementia. J Neurol Neurosurg Psychiatry 2004;75 (12) 1743- 1745PubMedGoogle ScholarCrossref 21. Whitwell JLJack CR JrParisi JE et al. Rates of cerebral atrophy differ in different degenerative pathologies. Brain 2007;130 (pt 4) 1148- 1158PubMedGoogle ScholarCrossref 22. Forman MSFarmer JJohnson JK et al. Frontotemporal dementia: clinicopathological correlations. Ann Neurol 2006;59 (6) 952- 962PubMedGoogle ScholarCrossref 23. Kertesz AMcMonagle PBlair MDavidson WMunoz DG The evolution and pathology of frontotemporal dementia. Brain 2005;128 (pt 9) 1996- 2005PubMedGoogle ScholarCrossref

Chao, Linda L.;Schuff, Norbert;Clevenger, Erin M.;Mueller, Susanne G.;Rosen, Howard J.;Gorno-Tempini, Maria L.;Kramer, Joel H.;Miller, Bruce L.;Weiner, Michael W.

doi: 10.1001/archneur.64.11.1619pmid: 17998444

Abstract Background Structural magnetic resonance imaging (MRI) has been used to investigate the in vivo pathology of frontotemporal lobar degeneration. However, few neuroimaging studies have focused on white matter (WM) alterations in this disease. Objectives To use volumetric MRI techniques to identify the patterns of WM atrophy in vivo in 2 clinical variants of frontotemporal lobar degeneration—frontotemporal dementia (FTD) and semantic dementia—and to compare the patterns of WM atrophy with those of gray matter (GM) atrophy in these diseases. Design Structural MRIs were obtained from patients with FTD (n = 12) and semantic dementia (n = 13) and in cognitively healthy age-matched controls (n = 24). Regional GM and WM were classified automatically from high-resolution T1-, T2-, and proton density–weighted MRIs with Expectation-Maximization Segmentation and compared between the groups using a multivariate analysis of covariance model that included age and WM lesion volumes as covariates. Results Patients with FTD had frontal WM atrophy and frontal, parietal, and temporal GM atrophy compared with controls, who had none. Patients with semantic dementia had temporal WM and GM atrophy and patients with FTD had frontal GM atrophy. Adding temporal WM volume to temporal GM volume significantly improved the discrimination between semantic dementia and FTD. Conclusions These results show that patients with frontotemporal lobar degeneration who are in relatively early stages of the disease (Clinical Dementia Rating score, 1.0-1.2) have WM atrophy that largely parallels the pattern of GM atrophy typically associated with these disorders. Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disease characterized by atrophy in the frontal and anterior temporal lobes. It accounts for 10% to 15% of all cases of dementia. The clinical manifestations and the patterns of anatomic involvement in FTLD are heterogeneous. For example, some patients have atrophy predominantly in the frontal lobe while others have more severe atrophy in the temporal lobe.1,2 The patterns of anatomic involvement in FTLD can also be asymmetric.2,3 The neuropathology of FTLD may be as heterogeneous as the clinical spectrum of symptoms. Although the histopathologic subtypes of FTLD have generally been divided into those with tau-positive inclusions and those without tau abnormalities, clinicopathological and genetic studies have recently revealed that the majority of sporadic and familial frontotemporal dementia (FTD) cases are not obviously associated with tau pathology and/or tau gene mutations. Furthermore, some studies have linked several autosomal dominantly inherited familial FTD cases to a variety of gene loci on different chromosomes.4 Structural magnetic resonance imaging (MRI) techniques have long been used to investigate the pathology of FTLD in vivo. However, most of the neuroimaging work to date has focused on gray matter (GM) alterations in the brain.5-9 Thus, little is known about the regional patterns of white matter (WM) atrophy in these diseases in vivo. The primary goal of our study was to use volumetric MRI techniques to identify the patterns of WM atrophy in vivo in 2 clinical variants of FTLD: FTD and semantic dementia. A secondary aim was to compare the patterns of WM atrophy with the patterns of GM atrophy in these diseases. Frontotemporal lobar degeneration has traditionally been regarded as a GM disease. However, there have been studies that reported WM pathology in FTLD. For example, 1 postmortem diffusion tensor imaging study reported WM abnormalities in the frontal lobes of a patient with FTD.10 Using tensor-based morphometry, our group has reported WM atrophy in vivo in the temporal lobes of patients with semantic dementia.11 Based on these findings, the first a priori hypothesis of our study is that patients with FTLD will exhibit significant WM atrophy relative to cognitively healthy control subjects. Because myelin breakdown and the subsequent loss of or damage to WM would likely exhibit a regional pattern consistent with known FTLD pathology, our second a priori hypothesis is that the patterns of WM atrophy in FTLD will parallel the patterns of GM atrophy. Frontotemporal dementia has been described as the “frontal lobe variant of FTLD”12 because of the prominent frontal lobe damage associated with this disease.5,6,13,14 However, GM atrophy in the temporal lobes has also been noted in FTD.6,8,9,15 For this reason, we expect patients with FTD to exhibit WM atrophy in the frontal and temporal lobes relative to cognitively healthy subjects. Previous studies have also noted frontal lobe GM atrophy in FTD compared with semantic dementia cases.8 Therefore, we also expect patients with FTD to exhibit frontal WM atrophy relative to patients with semantic dementia. Semantic dementia has long been associated with anterior temporal lobe atrophy.16,17 However, recent voxel-based morphometry studies have shown frontal lobe involvement in semantic dementia as well.7,8,18 For this reason, we expect patients with semantic dementia to exhibit WM atrophy primarily in the temporal lobes, but also in the frontal lobes, relative to cognitively healthy patients. Methods Study participants Twenty-five patients with FTLD (10 women; mean age, 63.6 years [SD, 7.0]) and 24 cognitively healthy control subjects (13 women; mean age, 66.3 years [SD, 10.8]) participated in the study. The patients with FTLD were recruited consecutively from the University of California San Francisco Memory and Aging Center. Of the 25 FLTD patients, 12 met Neary and colleagues’19 criteria for FTD: (1) insidious onset and gradual progression, (2) early decline in social, interpersonal conduct, (3) early impairment in regulation of personal conduct, (4) early emotional blunting, and (5) early loss of insight. Thirteen patients met Neary and colleagues’19 criteria for semantic dementia: (1) insidious onset and gradual progression, (2) a language disorder characterized by empty fluent speech, loss of word meaning, or semantic paraphasias, (3) a perceptual disorder characterized by impaired recognition of familiar faces or object identity, (4) preserved perceptual matching and drawing reproduction, (5) preserved single-word repetition, and (6) preserved ability to read aloud and write to dictation orthographically regular words. All patients were evaluated by a neurologist and a nurse and underwent neuropsychological evaluation to establish the pattern of cognitive and behavioral deficits. General intelligence was assessed using the Mini-Mental State Examination,20 and dementia severity was assessed using the Clinical Dementia Rating (CDR) scale.21 The 24 age-matched control subjects were chosen from participants enrolled in ongoing research on healthy aging at the University of California San Francisco Memory and Aging Center. The control subjects had no history of neurological or psychiatric disorders, no evidence of significant cognitive impairment (as confirmed by an informant), and no evidence of focal disease or subcortical WM ischemic changes on MRI. Control subjects underwent the same neuropsychological battery as the patient groups. The committees for human research at University of California San Francisco and the San Francisco VAMC approved the protocol. Informed consent was obtained from each participant or a legal representative prior to the study. Magnetic resonance imaging Coronal T1-weighted images (repetition time/inversion time/echo time = 9/300/4 milliseconds; 1 × 1 mm2 in-plane resolution; 1.5-mm slabs), axial proton density–weighted images, and T2-weighted images (repetition time/inversion time/echo time = 5000/20/85 milliseconds; 1 × 1.25 mm2 in-plane resolution; 3-mm slabs) were obtained on a clinical 1.5-T magnetic resonance scanner (Vision; Siemens Medical Solutions, Iselin, New Jersey). Tissue segmentation Cortical GM, WM, and cerebral spinal fluid were classified automatically using Expectation-Maximization Segmentation.22-24 Each subject's T2- and proton density–weighted images were interpolated to the resolution of their T1-weighted image (1 × 1 × 1.5 mm) and coregistered to the T1-weighted image using Automated Image Registration, version 3.0.25 These 3 coregistered images were then normalized to a customized T1-weighted template (resolution, 2 × 2 × 2 mm), which was derived from the MRIs of 64 subjects (30 women and 34 men; mean age, 56.6 years [SD, 18.6]) with an affine coregistration algorithm and used as input for segmentation with Expectation-Maximization Segmentation. Each subject's T2-weighted image, which has bright cerebral spinal fluid surrounding the brain, was used to capture the subarachnoid cerebral spinal fluid in its entirety. A customized digital brain atlas containing prior expectations about the spatial localization of different tissue classes was created from the same images used to create the T1-weighted template. These prior brain atlases were used to initialize the algorithm and to constrain the classification process during subsequent iterations. Figure 1A shows examples of a T1-weighted image and GM and WM segmentation in a control, FTD, and semantic dementia subject. The regional WM volumes analyzed in this study do not include WM signal hyperintensities or WM lesions, which Expectation-Maximization Segmentation identifies as outliers with respect to a statistical model of a healthy brain. Figure 1B shows an example of the spatial distribution of WM signal hyperintensities on a proton density–weighted image, as identified by Expectation-Maximization Segmentation. To account for variations in head size between subjects, all regional brain volumes were scaled to each subjects' total intracranial volume. The scaling for total intracranial volume was further normalized to the groups' overall mean total intracranial volume value (patients and controls combined). Total intracranial volume was computed by summing the volume of all voxels classified as cerebral spinal fluid or brain tissue from the frontal, parietal, temporal, and occipital lobes (ie, cortical and subcortical GM, WM, and WM signal hyperintensities). Tissue and cerebral spinal fluid from the cerebellum and brainstem were not included in the total intracranial volume calculation. Figure 1C shows an example of the lobar markings. Statistical analyses Data were analyzed with SPSS, version 12.0 (SPSS, Chicago, Illinois). Group differences in demographic and clinical variables were analyzed with a multivariate analysis of variance. In cases in which the omnibus multivariate analysis of variance yielded a significant main group effect, additional analyses of variance with the Tukey post hoc test were performed to further examine differences among FTD, semantic dementia, and control subjects. Because previous MRI studies have reported age-related26 and WM lesion–related27 GM and WM loss, age and WM lesion (ie, WM signal hyperintensities) volumes were included as covariates in multivariate analyses of covariance of the GM and WM volumes. Furthermore, a Shapiro-Wilks test of normality revealed that the regional WM and GM volumes approached a normal distribution when age was included as an additive term in the model. Because of systematic errors partially introduced by the left/right asymmetry of the template used for warping, we combined volume data from both hemispheres in the analyses. To further examine group difference, analyses of covariance with the Tukey post hoc test were performed in cases in which the omnibus multivariate analysis of covariance yielded a significant main group effect. We also estimated the powers of different regional WM volumetric measures to correctly differentiate each patient group from cognitively healthy controls and from each other (these were based on logistic regressions). Sensitivity and specificity of the classifications were expressed in terms of a receiver operator characteristics analysis as area under the curve. The logistic regressions were further adapted to a random leave-one-out procedure with 1000 runs for cross-validation of the classifications. The areas under the curve from the 1000 cross-validations were compared using Wilcoxon signed rank tests. The Wilcoxon signed rank test was also used to determine if the receiver operator characteristic distribution for GM volume was significantly different from the receiver operator characteristic for GM and WM volumes. These statistical computations were performed using Splus, version 6.3 (Insightful Corp, Seattle, Washington). Results Demographic and clinical data are summarized in Table 1. There were significant group differences for Mini-Mental State Examination, CDR, and CDR sum of boxes scores. Post hoc tests revealed that control subjects had higher Mini-Mental State Examination and lower CDR and CDR sum of boxes scores than the other patient groups. The volumetric MRI data and results of the multivariate analysis of covariance and the Tukey post hoc test statistics are summarized in Table 2. There were significant group effects for frontal (P < .01) and temporal (P < .001) WM volumes. Post hoc tests revealed that patients with FTD had less left frontal WM volume (P = .02) than controls and that patients with semantic dementia had less temporal WM volume than control and FTD patients (P < .001). There were main group effects for frontal, parietal, and temporal GM volumes. Post hoc tests revealed that patients with FTD had less frontal GM volume than controls (P < .001) and semantic dementia patients (P = .03); less parietal GM volume than controls (P = .02); and moderately (P = .051) less temporal GM volume than controls. Patients with semantic dementia had less frontal GM volume than controls (P = .01) and less temporal GM volume than controls and FTD patients (P < .001). Finally, we performed exploratory analyses to examine the value of adding regional WM volumes to regional GM volumes for correctly differentiating each patient group from controls and from each other. Adding regional WM volume to regional GM volume did not improve the classification of any patient group from controls. However, adding temporal WM volume to temporal GM volume improved differentiating between FTD and semantic dementia; overall classification improved from 88% to 96%, with the area under the curve improving from 0.90 to 0.98. A Wilcoxon signed rank test revealed significant differences between the areas under the 2 receiver operator characteristic curves (P = .002) (Figure 2). Comment The major findings of this study are that (1) patients with FTD had frontal WM atrophy compared with cognitively healthy controls, who had none; (2) patients with semantic dementia had temporal WM atrophy relative to cognitively healthy and FTD patients; (3) adding temporal WM volume to temporal GM volume significantly improved the discrimination between semantic dementia and FTD patients; and (4) patients with FTD had parietal GM atrophy compared with cognitively healthy patients. The primary goal of this study was to identify the regional patterns of WM atrophy in vivo in 2 subtypes of FTLD using volumetric MRI techniques. The first major finding is that FTD and semantic dementia patients exhibited significant WM atrophy. Compared with controls, FTD patients had WM atrophy in the frontal lobe, while semantic dementia patients had WM atrophy in the temporal lobes. This finding is consistent with previous reports of WM pathology in patients with FTLD10,14,28 and a postmortem diffusion tensor imaging study of an FTD patient that found WM abnormalities in the frontal lobes.10 Broe and colleagues29 have suggested that WM atrophy does not occur in FTD until later stages of the disease, roughly corresponding to CDR scores of 3 to 5, after the frontal and temporal lobes have been severely affected. However, the current results suggest that WM changes in FTD can be detected in vivo at earlier stages of the disease in patients with a mean CDR score of 1.2. At least 3 factors may contribute to these discrepant findings. First, we had smaller intervals between CDR rating and brain atrophy measurement. Second, the quantitative nature of the volumetric MRI analysis may be more sensitive than the visual atrophy measurements employed by Broe et al.29 Third, only 7 of the 24 cases that Broe and colleagues29 examined had CDR scores of 1 or 2 at the time of death. Moreover, the cause of death in each of these cases was a condition unrelated to FTD (eg, myocardial infarction and pulmonary embolism). Therefore, it may be possible that Broe et al29 did not have enough early FTD cases to detect significant WM changes. Using deformation morphometry, we had previously reported significant WM atrophy in the superior temporal gyrus and the middle and inferior temporal gyri of semantic dementia patients relative to controls.11 In the current study, we extend this finding by showing that patients with semantic dementia also have significant temporal lobe WM atrophy compared with FTD patients. Moreover, logistic regression analysis revealed that adding temporal WM volume to temporal GM volume significantly improved the discrimination between semantic dementia and FTD. However, this may simply be a reflection of the overweighting of pathologic changes in the temporal lobe in patients with semantic dementia. We hypothesized that WM atrophy would parallel GM atrophy in FTLD. However, the only region where patients with FTD showed significant GM and WM atrophy compared with controls was the frontal lobe. Patients with FTD also had significant GM but not WM atrophy in the parietal temporal lobes. Similarly, patients with semantic dementia had significant GM atrophy in temporal and frontal lobes, but only significant WM atrophy in the temporal lobe. One possible explanation for this pattern of results is that volumetric MRI cannot detect significant WM loss unless GM atrophy has reached a tipping point. For example, we only detected significant WM atrophy in regions of the brain where FTD and semantic dementia patients had the largest amount of GM atrophy (ie, > 10% more than controls). Moreover, when regional GM volumes were included as covariates in the analyses, the WM volume differences between FTLD and controls were no longer significant. One novel finding of this study is that patients with FTD had significant parietal GM atrophy compared with controls. Although parietal atrophy is not a feature typically associated with the disease, Figure 1A clearly shows parietal GM atrophy in the FTD patient compared with the control subject. Moreover, it is well known from research in monkeys that there are connections between some of the cytoarchitectonic areas of the parietal lobe and the anterior cingulate,30-33 frontal cortex,34,35 superior temporal sulcus, and the parahippocampal region.30,36,37 Given this intricate system of interconnections between frontal, parietal, and temporal neurons and that the frontal lobe and, to a lesser extent, the temporal lobe are affected in FTD, it should not be surprising that we also observed parietal GM atrophy in patients with FTD. One limitation to consider when interpreting our results is the method we used to quantify WM volumes. T1-, T2-, and proton density–weighted MRI scans were used to compute WM volumes from large regions of the brain. Future studies with diffusion tensor imaging will afford a better opportunity to examine WM changes associated with these neurodegenerative diseases in more clearly defined brain regions. Another limitation is that, though the patterns of anatomic involvement in FTLD can be asymmetric,2,3 we were unable to examine hemispheric differences in WM and GM volumes owing to systematic asymmetries introduced by the warping process that we used. However, future studies with other kinds of software (eg, FreeSurfer38) may afford a better opportunity to examine the effects of these neurodegenerative diseases on cortical thickness and different cortical and subcortical regions. A third limitation of our study is the uncertainty of the true diagnosis or the underlying histopathology subtype of the patients studied. However, careful follow-up of patients without a change in their clinical diagnoses may improve the gold standard in clinical studies.39 A fourth limitation is that these findings were obtained from relatively small groups of patients; thus, the results may not generalize to more heterogeneous cohorts. For this reason, our findings need to be validated prospectively in larger patient populations. Finally, it is difficult to assess disease severity and duration reliably in FTD and semantic dementia. Mini-Mental State Examination scores are disproportionately sensitive to language deficits, while CDR scores are sensitive to memory impairment; behavioral changes are difficult to quantify. Therefore, volume differences between the different dementia groups could, at least in part, be explained by impairment severity rather than by the type of dementia. These limitations notwithstanding, our results clearly show that there is WM atrophy in vivo in FTLD patients who are in relatively early (CDR score, 1.0-1.2) stages of the disease. We found WM atrophy primarily in brain regions where there was also substantial GM loss (ie, > 10% more than controls), which is suggestive of wallerian degeneration. However, we cannot rule out the possibility that the WM atrophy observed in our study is also partially caused by retrograde degeneration. Back to top Article Information Correspondence: Linda L. Chao, PhD, University of California, San Francisco, Department of Veterans Affairs Medical Center, 4150 Clement St (114M), San Francisco, CA 94121 ([email protected]). Accepted for Publication: February 28, 2007. Author Contributions: All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Chao, Schuff, Gorno-Tempini, Miller, and Weiner. Acquisition of data: Clevenger, Rosen, Gorno-Tempini, and Kramer. Analysis and interpretation of data: Chao, Schuff, Mueller, Gorno-Tempini, and Weiner. Drafting of the manuscript: Chao and Gorno-Tempini. Critical revision of the manuscript for important intellectual content: Chao, Schuff, Clevenger, Mueller, Rosen, Gorno-Tempini, Kramer, Miller, and Weiner. Statistical analysis: Chao, Schuff, and Gorno-Tempini. Obtained funding: Miller and Weiner. Administrative, technical, and material support: Chao, Clevenger, Mueller, Rosen, and Gorno-Tempini. Study supervision: Chao and Gorno-Tempini. Financial Disclosure: None reported. Funding/Support: This work was supported by grant P01 AG19724 from the National Institutes of Health. Additional Contributions: John Kornak, PhD, assisted with statistical analyses. References 1. Neary DSnowden JSMann DM The clinical pathological correlates of lobar atrophy. Dementia 1993;4 (3-4) 154- 159PubMedGoogle Scholar 2. Edwards-Lee TMiller BLBenson DF et al. The temporal variant of frontotemporal dementia. Brain 1997;120 (pt 6) 1027- 1040PubMedGoogle ScholarCrossref 3. Boone KBMiller BLLee ABerman NSherman DStuss DT Neuropsychological patterns in right versus left frontotemporal dementia. J Int Neuropsychol Soc 1999;5 (7) 616- 622PubMedGoogle ScholarCrossref 4. Tolnay MProbst A Frontotemporal lobar degeneration: tau as a pied piper? Neurogenetics 2002;4 (2) 63- 75PubMedGoogle ScholarCrossref 5. Frisoni GBBeltramello AGeroldi CWeiss CBianchetti ATrabucchi M Brain atrophy in frontemporal dementia. J Neurol Neurosurg Psychiatry 1996;61 (2) 157- 165PubMedGoogle ScholarCrossref 6. Fukui TKertesz A Volumetric study of lobar atrophy in Pick complex and Alzheimer's disease. J Neurol Sci 2000;174 (2) 111- 121PubMedGoogle ScholarCrossref 7. Mummery CJPatterson KPrice CJAshburner JFrackowiak RSHodges JR A voxel-based morphometry study of semantic dementia: relationship between temporal lobe atrophy and semantic memory. Ann Neurol 2000;47 (1) 36- 45PubMedGoogle ScholarCrossref 8. Rosen HJGorno-Tempini MLGoldman WP et al. Patterns of brain atrophy in frontotemporal dementia and semantic dementia. Neurology 2002;58 (2) 198- 208PubMedGoogle ScholarCrossref 9. Boccardi MLaakso MPBresciani L et al. The MRI pattern of frontal and temporal brain atrophy in fronto-temporal dementia. Neurobiol Aging 2003;24 (1) 95- 103PubMedGoogle ScholarCrossref 10. Larsson EMEnglund ESjobeck MLätt JBrockstedt S MRI with diffusion tensor imaging post-mortem at 3.0 T in a patient with frontotemporal dementia. Dement Geriatr Cogn Disord 2004;17 (4) 316- 319PubMedGoogle ScholarCrossref 11. Studholme CCardenas VBlumenfeld R et al. Deformation tensor morphometry of semantic dementia with quantitative validation. Neuroimage 2004;21 (4) 1387- 1398PubMedGoogle ScholarCrossref 12. Miller BLGearhart R Neuroimaging in the diagnosis of frontotemporal dementia. Dement Geriatr Cogn Disord 1999;10(suppl 1)71- 74PubMedGoogle ScholarCrossref 13. Kitagaki HMori EYamaji S et al. Frontotemporal dementia and Alzheimer disease: evaluation of cortical atrophy with automated hemispheric surface display generated with MR images. Radiology 1998;208 (2) 431- 439PubMedGoogle ScholarCrossref 14. Larsson EPassant USundgren PC et al. Magnetic resonance imaging and histopathology in dementia, clinically of frontotemporal type. Dement Geriatr Cogn Disord 2000;11 (3) 123- 134PubMedGoogle ScholarCrossref 15. Galton CJGomez-Anson BAntoun N et al. Temporal lobe rating scale: application to Alzheimer's disease and frontotemporal dementia. J Neurol Neurosurg Psychiatry 2001;70 (2) 165- 173PubMedGoogle ScholarCrossref 16. Hodges JRPatterson KOxbury SFunnell E Semantic dementia: progressive fluent aphasia with temporal lobe atrophy. Brain 1992;1151783- 1806PubMedGoogle ScholarCrossref 17. Snowden JSNeary DMann DMAGoulding PJTesta HJ Progressive language disorder due to lobar atrophy. Ann Neurol 1992;31 (2) 174- 183PubMedGoogle ScholarCrossref 18. Galton CJPatterson KGraham K et al. Differing patterns of temporal atrophy in Alzheimer's disease and semantic dementia. Neurology 2001;57 (2) 216- 225PubMedGoogle ScholarCrossref 19. Neary DSnowden JSGustafson L et al. Frontotemporal lobar degeneration: a consensus on clinical diagnosis criteria. Neurology 1998;51 (6) 1546- 1554PubMedGoogle ScholarCrossref 20. Folstein MFFolstein SEMcHugh PR “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12 (3) 189- 198PubMedGoogle ScholarCrossref 21. Morris JC Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. Int Psychogeriatr 1997;9(suppl 1)173- 176PubMedGoogle ScholarCrossref 22. Van Leemput KMaes FVandermeulen DSuetens P A unifying framework for partial volume segmentation of brain MR images. IEEE Trans Med Imaging 2003;22 (1) 105- 109PubMedGoogle ScholarCrossref 23. Van Leemput KMaes FVandermeulen DColchester ASuetens P Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 2001;20 (8) 677- 688PubMedGoogle ScholarCrossref 24. Van Leemput KMaes FVandermeulen DSuetens P Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging 1999;18 (10) 897- 908PubMedGoogle ScholarCrossref 25. Woods RPGrafton STHolmes CJCherry SRMazziotta JC Automated image registration, I: general methods and intrasubject, intramodality validation. J Comput Assist Tomogr 1998;22 (1) 139- 152PubMedGoogle ScholarCrossref 26. Ge YGrossman RIBabb JSRabin MLMannon LJKolson DL Age-related total gray matter and white matter changes in normal adult brain, part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol 2002;23 (8) 1327- 1333PubMedGoogle Scholar 27. Du ATSchuff NChao LL et al. White matter lesions are associated with cortical atrophy more than entorhinal and hippocampal atrophy. Neurobiol Aging 2005;26 (4) 553- 559PubMedGoogle ScholarCrossref 28. Englund EBrun A Frontal lobe degeneration of non-Alzheimer type, IV: white matter changes. Arch Gerontol Geriatr 1987;6 (3) 235- 243PubMedGoogle ScholarCrossref 29. Broe MHodges JRSchofield EShepherd CEKril JJHalliday GM Staging disease severity in pathologically confirmed cases of frontotemporal dementia. Neurology 2003;60 (6) 1005- 1011PubMedGoogle ScholarCrossref 30. Vogt BAGabriel M Neurobiology of Cingulate Cortex and Limbic Thalamus: A Comprehensive Handbook. Boston, MA: Birkhauser; 1993:249-284 31. Morecraft RJVan Hoesen GW Convergence of limbic input to the cingulate motor cortex in the rhesus monkey. Brain Res Bull 1998;45 (2) 209- 232PubMedGoogle ScholarCrossref 32. Morecraft RJVan Hoesen GW Cingulate input to the primary and supplementary motor cortices in the rhesus monkey: evidence for somatotopy in areas 24c and 23c. J Comp Neurol 1992;322 (4) 471- 489PubMedGoogle ScholarCrossref 33. Pandya DNKuypers HG Cortico-cortical connections in the rhesus monkey. Brain Res 1969;13 (1) 13- 36PubMedGoogle ScholarCrossref 34. Morris RPetrides MPandya DN Architecture and connections of retrosplenial area 30 in the rhesus monkey (Macaca mulatta). Eur J Neurosci 1999;11 (7) 2506- 2518PubMedGoogle ScholarCrossref 35. Carmichael STPrice JL Limbic connections of the orbital and medial prefrontal cortex in macaque monkeys. J Comp Neurol 1995;363 (4) 615- 641PubMedGoogle ScholarCrossref 36. Kobayashi YAmaral DG Macaque monkey retrosplenial cortex, II: cortical afferents. J Comp Neurol 2003;466 (1) 48- 79PubMedGoogle ScholarCrossref 37. Vogt BAPandya DN Cingulate cortex of the rhesus monkey, II: cortical afferents. J Comp Neurol 1987;262 (2) 271- 289PubMedGoogle ScholarCrossref 38. Fischl BSalat DHBusa E et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33 (3) 341- 355PubMedGoogle ScholarCrossref 39. Quizilbash NSchneider LSChui H et al Evidence-based Dementia Practice. eds Oxford, England: Blackwell Science; 2002

Sproule, Douglas M.;Kaufmann, Petra;Engelstad, Kristen;Starc, Thomas J.;Hordof, Allan J.;De Vivo, Darryl C.

doi: 10.1001/archneur.64.11.1625pmid: 17998445

Abstract Background Tissues with high energy demands, such as the heart, are susceptible to the effects of mitochondrial DNA point mutations. Objective To investigate the frequency of Wolff-Parkinson-White (WPW) syndrome among a phenotypically and genotypically homogeneous cohort of patients with MELAS (mitochondrial encephalopathy, lactic acidosis, and strokelike episodes) and the A3243G mutation most commonly associated with MELAS syndrome. Design Survey. Setting The Pediatric Neuromuscular Disease Center at Columbia University. Patients Thirty patients with the A3243G mutation and MELAS syndrome enrolled in a clinical trial to assess the effect of dichloroacetate on neurologic symptoms. Interventions Medical histories and electrocardiograms were reviewed and DNA samples from fibroblasts, urine and cheek epithelial cells, leukocytes, and hair were analyzed to determine mitochondrial mutation abundance and estimate total mutation burden. Results Four of 30 patients (13%) had a clinical history of, or electrocardiographic findings consistent with, WPW syndrome. In 2 patients, WPW syndrome preceded MELAS syndrome by 15 and 21 years. The tissue burden of mutant mitochondria was similar in patients with (49.4%) and without (39.1%) WPW syndrome. Conclusions The prevalence of WPW syndrome among patients with MELAS syndrome and the A3243G mutation appears much higher than in the normal population and may become manifest earlier than neurologic symptoms. Patients with WPW syndrome and neurologic abnormalities consistent with MELAS syndrome, such as seizures, deafness, short stature, and stroke, should be screened for the A3243G mutation. Moreover, patients with MELAS syndrome should be monitored for cardiac anomalies including cardiomyopathy and WPW syndrome. Mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes (MELAS) is a maternally inherited disorder characterized by a progressive encephalopathy punctuated by episodes of focal brain injury.1,2 An A→G transition at nucleotide 3243 of the mitochondrial genome, affecting a mitochondrial leucine tRNA gene, is the most common underlying mutation and results in impaired oxidative phosphorylation.3 Multiple organs can be affected, but tissues with high energy demand are most vulnerable. Therefore, nervous system and muscle impairment often dominate the clinical picture.1-3 Cardiac involvement has been described in patients with MELAS syndrome, including hypertrophic and dilated cardiomyopathy,4,5 and conduction disturbances, including Wolff-Parkinson-White (WPW) syndrome.1,2,6 However, to our knowledge, no large case series has directly addressed the association, coincidence, and relative onset of these disorders among a genotypically well-described cohort. Herein we describe the occurrence of WPW syndrome among a cohort of patients with MELAS syndrome and the A3243G mutation. The patients were enrolled in a clinical trial evaluating dichloroacetate as a treatment for MELAS.7 In addition, we attempt to characterize a temporal relationship in the clinical onset of WPW and MELAS among patients who are doubly affected and discuss why a mitochondrial DNA mutation such as the A3243G mutation might be associated with the WPW syndrome. Methods Patients for this study were participants in a clinical trial assessing the efficacy of dichloroacetate in the treatment of MELAS. Recruitment for this trial commenced on March 1, 2000, and included US patients who were evaluated at Columbia University Medical Center, who were referred by other physicians, or who learned of the trial through publications, Web sites, or nonprofit patient advocacy groups. The methods and selection criteria used in this trial have been described previously.7 Criteria for inclusion required the presence of the MELAS syndrome (including seizures or strokelike episodes); confirmed A3243G mitochondrial DNA point mutation as assessed with polymerase chain reaction; and elevated lactate values in the ventricles as measured by magnetic resonance spectroscopy or in the cerebrospinal fluid as measured by lumbar puncture. Excluded were patients younger than 6 years, those with elevated serum transaminase levels (alanine aminotransferase or aspartate aminotransferase), and those with an inability to complete study procedures. All 30 patients enrolled in the clinical trial were included in this study. Age at onset of MELAS syndrome was described as the age at which the patient experienced his or her first seizure or strokelike event based on clinical history and medical records. Electrocardiograms were obtained on all patients, concurrent with identification of any prior cardiac history, including past cardiac surgery or catheter ablations. In patients with a history of a past catheter ablation, case histories and preablation electrocardiograms were reviewed to confirm the history of WPW syndrome. Electrocardiograms were reviewed and analyzed independently by 2 pediatric cardiologists, one who interacted with the patients and was aware of the MELAS diagnosis (T.J.S.), and a second reviewer who was blinded to patient history (A.J.H.). In cases of discordance between the 2 reviewers, the blinded reviewer's opinion was accepted. Tissue samples from patients enrolled in this study were obtained from skin fibroblasts, blood leukocytes, hair, oral mucosa, and urine sediment cells. Mutant mitochondrial DNA in each tissue type was quantified as a percentage of total mitochondrial DNA using polymerase chain reaction amplification and restriction fragment length polymorphism analysis of total DNA, which involved using a technique described previously.8 A total tissue burden was estimated using a mean of the percentage of mutant mitochondrial DNA in the 5 tissue types, where available. Results Demographic characteristics and clinical features of the cohort are outlined in the Table. Patients with WPW syndrome tended to be younger and to have disease onset before age 40 years. A nonsignificant increase in the mean composition of mutant mitochondrial DNA was observed among patients with WPW (49.4%) compared with those without WPW (39.1%). Wolff-Parkinson-White syndrome was reported in 4 of 30 patients (13%) enrolled in this study. Of the 4 cases, 1 was identified by electrocardiography demonstrating a short PR interval, delta wave, and ST-segment changes, with a clinical history of tachycardia. This patient had no history of electrocardiographic abnormalities and was identified via review of his enrollment electrocardiogram. Three additional cases were determined through patient history of past documented WPW syndrome treated with catheter ablation of a bypass tract. Diagnosis and treatment in these 3 cases was made before, and independent of, the initial diagnosis of MELAS syndrome at a mean age of 10 years (range, 12 weeks to 14 years). The frequency of WPW syndrome in this cohort is significantly higher than in the general population (P < .001 vs historical control of 1.5 to 3.1 per 1000 persons using Fisher exact test9). In 3 individuals with a history of WPW syndrome, this diagnosis preceded the diagnosis of MELAS syndrome. In 1 patient, WPW syndrome was diagnosed after the development of episodic tachycardia at 10 years of age. Within a few months of this diagnosis, this patient developed intractable seizures and was determined to have MELAS syndrome. In the other 2 patients, WPW syndrome became clinically apparent years before the development of MELAS syndrome. One patient was diagnosed as having WPW syndrome at 14 years of age and underwent an ablation at 24 years. He did not develop clinical manifestations of MELAS syndrome until he was 35 years of age. The other was diagnosed as having WPW syndrome at 12 weeks of age and underwent ablation at 12 years, but did not manifest MELAS syndrome until age 15 years. Comment In this study, we documented WPW syndrome in 4 of 30 patients with MELAS syndrome and the A3243G mutation. Wolff-Parkinson-White syndrome has been noted in previous case series on MELAS syndrome.1,2,6 In their review of published cases, Hirano et al2 noted that 6 of 43 patients with WPW syndrome and another 3 of 47 patients presented with cardiac conduction block. Okajima et al6 reported WPW syndrome in 3 of 11 pediatric patients with MELAS syndrome (10 of whom carried the A3243G mutation). The present study supports these previous observations of the association between the WPW and MELAS syndromes. This study examines an older cohort of patients with MELAS syndrome who exclusively express the A3243G mutation and, in so doing, incorporates a study group with distinctly different characteristics than the patient group studied by Okajima et al. With a previously noted prevalence of approximately 1.5 to 3.1 per 1000 persons in Western countries, WPW is a common cause of supraventricular tachycardia.9 A mutation of the PRKAG2 gene, an adenosine monophosphate–activated protein kinase mapped to the locus 7q34-q36,9 has been linked to the development of WPW syndrome in 2 families with an autosomal dominant form of the disorder.9 The adenosine monophosphate–activated protein kinase encoded by the PRKAG2 gene has been described as a “cellular fuel gauge.”10 Aberrant energy metabolism in the developing fetus, leading to an altered cellular responsiveness to energy-depleting stressors, has been proposed as a possible mechanism underlying the pathogenesis of this mutation.9 Perhaps it is the failure of this gauge to properly regulate energy metabolism that underlies the development of this disorder. A mitochondrial defect may act in a similar manner to create a relatively energy-depleted state, preventing the normal maturation of the insulating ring, thus leading to the generation of an abnormal conductive circuit. Why WPW has not been reported in increased frequency in other mitochondrial DNA-associated syndromes such as myoclonus epilepsy associated with ragged-red fibers (MERRF), neuropathy, ataxia, and retinitis pigmentosa (NARP), and Leigh syndrome is unknown. An understanding at the molecular and cellular levels of the importance of energy utilization and mitochondrial disease in the development of WPW syndrome remains a direction for future research. Although limited in sample size and retrospective in design, our study describes the possibility of WPW syndrome to precede the manifestation of MELAS syndrome in cases in which the 2 syndromes are present concurrently. Therefore, in patients with WPW syndrome and a clinical or family history suggestive of possible mitochondrial DNA disease, an underlying A3243G mutation should be considered. Also, given the potential for significant morbidity and mortality, patients with MELAS syndrome should be closely monitored for the development or existence of cardiac anomalies including cardiomyopathy, cardiac defects, and preexcitation syndromes such as WPW. Back to top Article Information Correspondence: Darryl C. De Vivo, MD, Neurological Institute, 710 W 168th St, Room 2-201, New York, NY 10032. Accepted for Publication: January 8, 2007. Author Contributions:Study concept and design: Kaufman, Starc, and De Vivo. Acquisition of data: Sproule, Kaufman, Engelstad, Starc, and De Vivo. Analysis and interpretation of data: Sproule, Kaufman, Starc, Hordof, and De Vivo. Drafting of the manuscript: Sproule, Kaufman, Engelstad, and De Vivo. Critical revision of the manuscript for important intellectual content: Kaufman, Starc, Hordof, and De Vivo. Statistical analysis: Starc. Obtained funding: De Vivo. Administrative, technical, and material support: De Vivo. Study supervision: Kaufman, Starc, Hordof, and De Vivo. Financial Disclosure: None reported. Funding/Support: This study was supported by grant K12 RR017648 (Dr Kaufmann) from the National Institutes of Health, grant PO1-HD32062 (Dr De Vivo) from the National Institute of Child Health and Human Development, and by the Colleen Giblin Foundation (Dr De Vivo). Dr Kaufmann is the recipient of an Irving Research Fellowship. References 1. Pavlakis SGPhillips PCDiMauro SDe Vivo DCRowland LP Mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes: a distinctive clinical syndrome. Ann Neurol 1984;16 (4) 481- 488PubMedGoogle ScholarCrossref 2. Hirano MRicci EKoenigsberger MR et al. MELAS: an original case and clinical criteria for diagnosis. Neuromuscul Disord 1992;2 (2) 125- 135PubMedGoogle ScholarCrossref 3. Kobayashi YMomoi MYTominaga K et al. Respiration-deficient cells are caused by a single point mutation in the mitochondrial tRNA-leu (UUR) gene in mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes (MELAS). Am J Hum Genet 1991;49 (3) 590- 599PubMedGoogle Scholar 4. Sato WTanaka MSugiyama S et al. Cardiomyopathy and angiopathy in patients with mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes. Am Heart J 1994;128 (4) 733- 741PubMedGoogle ScholarCrossref 5. Yoneda MTanaka MNishikimi M et al. Pleiotropic molecular defects in energy-transducing complexes in mitochondrial encephalomyopathy (MELAS). J Neurol Sci 1989;92 (2-3) 143- 158PubMedGoogle ScholarCrossref 6. Okajima YTanabe YTakayanagi MAotsuka H A follow up study of myocardial involvement in patients with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS). Heart 1998;80 (3) 292- 295PubMedGoogle Scholar 7. Kaufmann PEngelstad KWei Y et al. Dichloroacetate causes toxic neuropathy in MELAS: a randomized, controlled clinical trial. Neurology 2006;66 (3) 324- 330PubMedGoogle ScholarCrossref 8. Kaufmann PKoga YShanske S et al. Mitochondrial DNA and RNA processing in MELAS. Ann Neurol 1996;40 (2) 172- 180PubMedGoogle ScholarCrossref 9. Gollob MHGreen MSTang AS et al. Identification of a gene responsible for familial Wolff-Parkinson-White syndrome. N Engl J Med 2001;344 (24) 1823- 1831PubMedGoogle ScholarCrossref 10. Hardie DGCarling D The AMP-activated protein kinase: fuel gauge of the mammalian cell? Eur J Biochem 1997;246 (2) 259- 273PubMedGoogle ScholarCrossref